Externalizing Research Synthesis and Validation in AI Scientists through a Research Harness
Paper • 2606.18874 • Published • 6
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id string | area_id string | area string | area_tr string | rank_in_area int64 | title string | authors string | n_authors int64 | date string | year int64 | venue string | arxiv_id string | arxiv_url string | pdf_url string | hf_paper_url string | doi string | semantic_scholar_url string | citations int64 | influential_citations int64 | code_url string | code_source string | github_repo string | github_stars int64 | github_forks int64 | github_license string | github_language string | github_last_push string | github_archived bool | code_verified_on string | relevance_tier int64 | relevance string | why_for_cekicin string | is_2025_26_update bool | tasks string | abstract string | source string | score float64 |
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CKC-A01-001 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 1 | CarDD: A New Dataset for Vision-based Car Damage Detection | Xinkuang Wang; Wenjing Li; Zhongcheng Wu | 3 | 2022-11-02 | 2,022 | IEEE transactions on intelligent transportation systems (Print) | 2211.00945 | https://arxiv.org/abs/2211.00945 | https://arxiv.org/pdf/2211.00945 | https://huggingface.co/papers/2211.00945 | 10.1109/TITS.2023.3258480 | https://www.semanticscholar.org/paper/a80a7c2e0a4f77f3e0e2beba87bb010efd896a79 | 70 | 13 | https://github.com/CarDD-USTC/CarDD-USTC.github.io | PwC archive – official | CarDD-USTC/CarDD-USTC.github.io | 71 | 17 | none/unknown | Jupyter Notebook | 2023-09-06 | false | 2026-10-16 | 1 | core (Çekicin-specific) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Automatic car damage detection has attracted significant attention in the car insurance business. However, due to the lack of high-quality and publicly available datasets, we can hardly learn a feasible model for car damage detection. To this end, we contribute with Car Damage Detection (CarDD), the first public large-... | Papers with Code archive (Jul 2025) | 9.701 | |
CKC-A01-002 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 2 | Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes | Anthony Gosselin; Ge Ya Luo; Luis Lara; Florian Golemo; Derek Nowrouzezahrai; Liam Paull; et al. | 8 | 2025-05-30 | 2,025 | arXiv.org | 2506.00227 | https://arxiv.org/abs/2506.00227 | https://arxiv.org/pdf/2506.00227 | https://huggingface.co/papers/2506.00227 | 10.48550/arXiv.2506.00227 | https://www.semanticscholar.org/paper/faaa62dbe92c4fb6a102ba85da4ed0a3535ad46b | 5 | 1 | https://github.com/AnthonyGosselin/Ctrl-Crash | arXiv search + README/HF match (check it is the authors' code) | AnthonyGosselin/Ctrl-Crash | 55 | 4 | MIT | Python | 2025-06-03 | false | 2026-10-16 | 1 | core (Çekicin-specific) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Video diffusion techniques have advanced significantly in recent years; however, they struggle to generate realistic imagery of car crashes due to the scarcity of accident events in most driving datasets. Improving traffic safety requires realistic and controllable accident simulations. To tackle the problem, we propos... | arXiv search + code lookup | 8.804 | |
CKC-A01-003 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 3 | An Anti-fraud System for Car Insurance Claim Based on Visual Evidence | Pei Li; Bingyu Shen; Weishan Dong | 3 | 2018-04-30 | 2,018 | arXiv.org | 1804.11207 | https://arxiv.org/abs/1804.11207 | https://arxiv.org/pdf/1804.11207 | https://huggingface.co/papers/1804.11207 | https://www.semanticscholar.org/paper/34f2aef5aa519d20379037259645d4c84526662c | 40 | 2 | https://github.com/mingmin0101/Car-Damage-Detection | arXiv search + README/HF match (check it is the authors' code) | mingmin0101/Car-Damage-Detection | 6 | 0 | none/unknown | HTML | 2021-02-05 | false | 2026-10-16 | 1 | core (Çekicin-specific) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Automatically scene understanding using machine learning algorithms has been widely applied to different industries to reduce the cost of manual labor. Nowadays, insurance companies launch express vehicle insurance claim and settlement by allowing customers uploading pictures taken by mobile devices. This kind of insur... | arXiv search + code lookup | 7.687 | ||
CKC-A01-004 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 4 | CrashSplat: 2D to 3D Vehicle Damage Segmentation in Gaussian Splatting | Dragoş-Andrei Chileban; Andrei-Ştefan Bulzan; Cosmin Cernǎzanu-Glǎvan | 3 | 2025-09-28 | 2,025 | Symposium on Symbolic and Numeric Algorithms for Scientific Computing | 2509.23947 | https://arxiv.org/abs/2509.23947 | https://arxiv.org/pdf/2509.23947 | https://huggingface.co/papers/2509.23947 | 10.1109/SYNASC69064.2025.00037 | https://www.semanticscholar.org/paper/0b9b374e7eb90bcd45265ae15ccaa9160ec92344 | 1 | 0 | https://github.com/DragosChileban/CrashSplat | arXiv search + README/HF match (check it is the authors' code) | DragosChileban/CrashSplat | 2 | 0 | none/unknown | Python | 2025-10-12 | false | 2026-10-16 | 1 | core (Çekicin-specific) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | true | Automatic car damage detection has been a topic of significant interest for the auto insurance industry as it promises faster, accurate, and cost-effective damage assessments. However, few works have gone beyond 2D image analysis to leverage 3D reconstruction methods, which have the potential to provide a more comprehe... | arXiv search + code lookup | 6.242 | |
CKC-A01-005 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 5 | Small Dents, Big Impact: A Dataset and Deep Learning Approach for Vehicle Dent Detection | Danish Zia Baig; Mohsin Kamal; Zahid Ullah | 3 | 2025-08-21 | 2,025 | arXiv | 2508.15431 | https://arxiv.org/abs/2508.15431 | https://arxiv.org/pdf/2508.15431 | https://huggingface.co/papers/2508.15431 | https://www.semanticscholar.org/paper/7cf846b7c7f382a595b8ff1d3f77429be7bbfe38 | 1 | 0 | https://github.com/cyr0109/hotai-irent | arXiv search + README/HF match (check it is the authors' code) | cyr0109/hotai-irent | 0 | 0 | none/unknown | HTML | 2026-10-13 | false | 2026-10-16 | 1 | core (Çekicin-specific) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | true | Conventional car damage inspection techniques are labor-intensive, manual, and frequently overlook tiny surface imperfections like microscopic dents. Machine learning provides an innovative solution to the increasing demand for quicker and more precise inspection methods. The paper uses the YOLOv8 object recognition fr... | arXiv search + code lookup | 5.693 | ||
CKC-A01-006 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 6 | C-DiffDet+: Fusing Global Scene Context with Generative Denoising for High-Fidelity Car Damage Detection | Abdellah Zakaria Sellam; Ilyes Benaissa; Salah Eddine Bekhouche; Abdenour Hadid; Vito Renó; Cosimo Distante | 6 | 2025-08-30 | 2,025 | arXiv.org | 2509.00578 | https://arxiv.org/abs/2509.00578 | https://arxiv.org/pdf/2509.00578 | https://huggingface.co/papers/2509.00578 | 10.48550/arXiv.2509.00578 | https://www.semanticscholar.org/paper/6a30e838a3f5e66f34130b92afbc2234bc86caca | 0 | 0 | https://github.com/ilyesbenaissa/c_diffdet_plus | arXiv search + README/HF match (check it is the authors' code) | ilyesbenaissa/c_diffdet_plus | 0 | 1 | MIT | Python | 2026-03-16 | false | 2026-10-16 | 1 | core (Çekicin-specific) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | true | Fine-grained object detection in challenging visual domains, such as vehicle damage assessment, presents a formidable challenge even for human experts to resolve reliably. While DiffusionDet has advanced the state-of-the-art through conditional denoising diffusion, its performance remains limited by local feature condi... | arXiv search + code lookup | 5 | |
CKC-A01-007 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 7 | Grounding Agentic VLMs with Dedicated Segmentation for Fine-Grained Vehicle Damage Assessment | Vishwajeet Shivaji Hogale; Anjali Pai; Nitya Ravi | 3 | 2026-08-03 | 2,026 | arXiv | 2608.02470 | https://arxiv.org/abs/2608.02470 | https://arxiv.org/pdf/2608.02470 | https://huggingface.co/papers/2608.02470 | https://www.semanticscholar.org/paper/9939e09bbb9b20c4bd2b4b6e878c7a35b9e23bf6 | 0 | 0 | https://github.com/VichoRamirez/sm | arXiv search + README/HF match (check it is the authors' code) | VichoRamirez/sm | 0 | 0 | none/unknown | 2026-10-12 | false | 2026-10-16 | 1 | core (Çekicin-specific) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | true | Vision-language models (VLMs) are increasingly deployed as reasoning agents in real-world visual assessment pipelines, yet their spatial grounding remains unreliable for fine-grained, visually ambiguous targets. We study this gap in the context of automated vehicle damage assessment, where fine-grained defects such as ... | arXiv search + code lookup | 5 | |||
CKC-A01-008 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 8 | Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone | Hiroya Maeda; Yoshihide Sekimoto; Toshikazu Seto; Takehiro Kashiyama; Hiroshi Omata | 5 | 2018-01-29 | 2,018 | Comput. Aided Civ. Infrastructure Eng. | 1801.09454 | https://arxiv.org/abs/1801.09454 | https://arxiv.org/pdf/1801.09454 | https://huggingface.co/papers/1801.09454 | 10.1111/mice.12387 | https://www.semanticscholar.org/paper/04c49b649b8f4336c1f38f69b8381249c81fbf0d | 885 | 38 | https://github.com/sekilab/RoadDamageDetector | PwC archive – official | sekilab/RoadDamageDetector | 999 | 280 | MIT | Jupyter Notebook | 2025-10-23 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | GPU; object-detection; Object Detection; Road Damage Detection | Research on damage detection of road surfaces using image processing techniques has been actively conducted, achieving considerably high detection accuracies. Many studies only focus on the detection of the presence or absence of damage. However, in a real-world scenario, when the road managers from a governing body ne... | Papers with Code archive (Jul 2025) | 12.041 |
CKC-A01-009 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 9 | Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection | Fan Yang; Lei Zhang; Sijia Yu; Danil Prokhorov; Xue Mei; Haibin Ling | 6 | 2019-01-18 | 2,019 | IEEE transactions on intelligent transportation systems (Print) | 1901.06340 | https://arxiv.org/abs/1901.06340 | https://arxiv.org/pdf/1901.06340 | https://huggingface.co/papers/1901.06340 | 10.1109/TITS.2019.2910595 | https://www.semanticscholar.org/paper/2b7c208c34dd4a186f5d6d327bc94ec7397cc766 | 1,098 | 105 | https://github.com/fyangneil/pavement-crack-detection | PwC archive – official | fyangneil/pavement-crack-detection | 428 | 82 | none/unknown | C++ | 2025-02-22 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Edge Detection; Semantic Segmentation | Pavement crack detection is a critical task for insuring road safety. Manual crack detection is extremely time-consuming. Therefore, an automatic road crack detection method is required to boost this progress. However, it remains a challenging task due to the intensity inhomogeneity of cracks and complexity of the back... | Papers with Code archive (Jul 2025) | 11.833 |
CKC-A01-010 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 10 | BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response | Hongruixuan Chen; Jian Song; Olivier Dietrich; Clifford Broni-Bediako; Weihao Xuan; Junjue Wang; et al. | 12 | 2025-01-10 | 2,025 | Earth System Science Data | 2501.06019 | https://arxiv.org/abs/2501.06019 | https://arxiv.org/pdf/2501.06019 | https://huggingface.co/papers/2501.06019 | 10.48550/arXiv.2501.06019 | https://www.semanticscholar.org/paper/a87e2a61e8d818f399904e2e4949c0d0763d14db | 120 | 5 | https://github.com/ChenHongruixuan/BRIGHT | PwC archive – official | ChenHongruixuan/BRIGHT | 259 | 44 | Apache-2.0 | Python | 2026-07-21 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | All; Building change detection for remote sensing images; Building Damage Assessment; Change Detection; Change detection for remote sensing images; Disaster Response; Earth Observation | Disaster events occur around the world and cause significant damage to human life and property. Earth observation (EO) data enables rapid and comprehensive building damage assessment (BDA), an essential capability in the aftermath of a disaster to reduce human casualties and to inform disaster relief efforts. Recent re... | Papers with Code archive (Jul 2025) | 11.376 |
CKC-A01-011 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 11 | RDD2022: A multi-national image dataset for automatic Road Damage Detection | Deeksha Arya; Hiroya Maeda; Sanjay Kumar Ghosh; Durga Toshniwal; Yoshihide Sekimoto | 5 | 2022-09-18 | 2,022 | Geoscience Data Journal | 2209.08538 | https://arxiv.org/abs/2209.08538 | https://arxiv.org/pdf/2209.08538 | https://huggingface.co/papers/2209.08538 | 10.1002/gdj3.260 | https://www.semanticscholar.org/paper/844f8d0d8f34c8dd22d1ed6c0518677249882783 | 377 | 17 | https://github.com/sekilab/RoadDamageDetector | PwC archive – official | sekilab/RoadDamageDetector | 999 | 280 | MIT | Jupyter Notebook | 2025-10-23 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | object-detection; Object Detection; Road Damage Detection | The data article describes the Road Damage Dataset, RDD2022, which comprises 47,420 road images from six countries, Japan, India, the Czech Republic, Norway, the United States, and China. The images have been annotated with more than 55,000 instances of road damage. Four types of road damage, namely longitudinal cracks... | Papers with Code archive (Jul 2025) | 11.189 |
CKC-A01-012 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 12 | Segmentation-Based Deep-Learning Approach for Surface-Defect Detection | Domen Tabernik; Samo Šela; Jure Skvarč; Danijel Skočaj | 4 | 2019-03-20 | 2,019 | Journal of Intelligent Manufacturing | 1903.08536 | https://arxiv.org/abs/1903.08536 | https://arxiv.org/pdf/1903.08536 | https://huggingface.co/papers/1903.08536 | 10.1007/s10845-019-01476-x | https://www.semanticscholar.org/paper/b77b1fc188f3834541efbb75add8f1ea80fd6225 | 990 | 49 | https://github.com/skokec/segdec-net-jim2019 | PwC archive – official | skokec/segdec-net-jim2019 | 141 | 46 | NOASSERTION | Python | 2026-04-20 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Deep Learning; Defect Detection | Automated surface-anomaly detection using machine learning has become an interesting and promising area of research, with a very high and direct impact on the application domain of visual inspection. Deep-learning methods have become the most suitable approaches for this task. They allow the inspection system to learn ... | Papers with Code archive (Jul 2025) | 11.177 |
CKC-A01-013 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 13 | xBD: A Dataset for Assessing Building Damage from Satellite Imagery | Ritwik Gupta; Richard Hosfelt; Sandra Sajeev; Nirav Patel; Bryce Goodman; Jigar Doshi; et al. | 9 | 2019-11-21 | 2,019 | arXiv.org | 1911.09296 | https://arxiv.org/abs/1911.09296 | https://arxiv.org/pdf/1911.09296 | https://huggingface.co/papers/1911.09296 | https://www.semanticscholar.org/paper/8fbf011af21921a553bd8b20cd6eb16897f07801 | 423 | 77 | https://github.com/DIUx-xView/xView2_baseline | PwC archive – official | DIUx-xView/xView2_baseline | 243 | 92 | NOASSERTION | Python | 2023-07-26 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | 2D Semantic Segmentation; Building Damage Assessment; Change Detection; Disaster Response; Humanitarian | We present xBD, a new, large-scale dataset for the advancement of change detection and building damage assessment for humanitarian assistance and disaster recovery research. Natural disaster response requires an accurate understanding of damaged buildings in an affected region. Current response strategies require in-pe... | Papers with Code archive (Jul 2025) | 10.598 | |
CKC-A01-014 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 14 | Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms | Nachuan Ma; Jiahe Fan; Wenshuo Wang; Jin Wu; Yu Jiang; Lihua Xie; et al. | 7 | 2022-04-28 | 2,022 | Transportation Safety and Environment | 2204.13590 | https://arxiv.org/abs/2204.13590 | https://arxiv.org/pdf/2204.13590 | https://huggingface.co/papers/2204.13590 | 10.1093/tse/tdac026 | https://www.semanticscholar.org/paper/50ac326df96467408c7531446e9c57e2d9ac4407 | 137 | 5 | https://github.com/sekilab/RoadDamageDetector | PwC archive – official | sekilab/RoadDamageDetector | 999 | 280 | MIT | Jupyter Notebook | 2025-10-23 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Articles; Segmentation; Semantic Segmentation; Survey | Computer vision algorithms have been prevalently utilized for 3-D road imaging and pothole detection for over two decades. Nonetheless, there is a lack of systematic survey articles on state-of-the-art (SoTA) computer vision techniques, especially deep learning models, developed to tackle these problems. This article f... | Papers with Code archive (Jul 2025) | 10.181 |
CKC-A01-015 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 15 | FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding | Maryam Rahnemoonfar; Tashnim Chowdhury; Argho Sarkar; Debvrat Varshney; Masoud Yari; Robin Murphy | 6 | 2020-12-05 | 2,020 | IEEE Access | 2012.02951 | https://arxiv.org/abs/2012.02951 | https://arxiv.org/pdf/2012.02951 | https://huggingface.co/papers/2012.02951 | 10.1109/ACCESS.2021.3090981 | https://www.semanticscholar.org/paper/af2550268c34fb0376a3f18f150bf4f9ce3023b9 | 498 | 50 | https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021 | PwC archive – community implementation | BinaLab/FloodNet-Challenge-EARTHVISION2021 | 67 | 14 | none/unknown | 2021-03-26 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | image-classification; Image Classification; object-detection; Object Detection; Question Answering; Scene Understanding; Segmentation; Semantic Segmentation | Visual scene understanding is the core task in making any crucial decision in any computer vision system. Although popular computer vision datasets like Cityscapes, MS-COCO, PASCAL provide good benchmarks for several tasks (e.g. image classification, segmentation, object detection), these datasets are hardly suitable f... | Papers with Code archive (Jul 2025) | 9.822 | |
CKC-A01-016 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 16 | Transfer Learning-based Road Damage Detection for Multiple Countries | Deeksha Arya; Hiroya Maeda; Sanjay Kumar Ghosh; Durga Toshniwal; Alexander Mraz; Takehiro Kashiyama; et al. | 7 | 2020-08-30 | 2,020 | arXiv.org | 2008.13101 | https://arxiv.org/abs/2008.13101 | https://arxiv.org/pdf/2008.13101 | https://huggingface.co/papers/2008.13101 | https://www.semanticscholar.org/paper/47b80abd2d314011fe8c209b0b83724f76842766 | 89 | 7 | https://github.com/sekilab/RoadDamageDetector | PwC archive – official | sekilab/RoadDamageDetector | 999 | 280 | MIT | Jupyter Notebook | 2025-10-23 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Road Damage Detection; Transfer Learning | Many municipalities and road authorities seek to implement automated evaluation of road damage. However, they often lack technology, know-how, and funds to afford state-of-the-art equipment for data collection and analysis of road damages. Although some countries, like Japan, have developed less expensive and readily a... | Papers with Code archive (Jul 2025) | 9.754 | |
CKC-A01-017 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 17 | Hybrid-Segmentor: A Hybrid Approach to Automated Fine-Grained Crack Segmentation in Civil Infrastructure | June Moh Goo; Xenios Milidonis; Alessandro Artusi; Jan Boehm; Carlo Ciliberto | 5 | 2024-09-04 | 2,024 | Automation in Construction | 2409.02866 | https://arxiv.org/abs/2409.02866 | https://arxiv.org/pdf/2409.02866 | https://huggingface.co/papers/2409.02866 | 10.1016/j.autcon.2024.105960 | https://www.semanticscholar.org/paper/8782c1e52dd426bb28ecfce71a934705aaa6e886 | 79 | 7 | https://github.com/junegoo94/Hybrid-Segmentor | PwC archive – official | junegoo94/Hybrid-Segmentor | 57 | 8 | MIT | Python | 2025-06-30 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Crack Segmentation; Decoder; Segmentation; Semantic Segmentation | Detecting and segmenting cracks in infrastructure, such as roads and buildings, is crucial for safety and cost-effective maintenance. In spite of the potential of deep learning, there are challenges in achieving precise results and handling diverse crack types. With the proposed dataset and model, we aim to enhance cra... | Papers with Code archive (Jul 2025) | 9.212 |
CKC-A01-018 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 18 | RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment | Maryam Rahnemoonfar; Tashnim Chowdhury; Robin Murphy | 3 | 2022-02-24 | 2,022 | Scientific Data | 2202.12361 | https://arxiv.org/abs/2202.12361 | https://arxiv.org/pdf/2202.12361 | https://huggingface.co/papers/2202.12361 | 10.1038/s41597-023-02799-4 | https://www.semanticscholar.org/paper/e271deecb7c02a0f16c575ff05c81ed088ce842a | 142 | 5 | https://github.com/BinaLab/RescueNet-A-High-Resolution-Post-Disaster-UAV-Dataset-for-Semantic-Segmentation | PwC archive – official | BinaLab/RescueNet-A-High-Resolution-Post-Disaster-UAV-Dataset-for-Semantic-Segmentation | 110 | 25 | MIT | Python | 2024-05-17 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Scene Understanding; Segmentation; Semantic Segmentation | Recent advancements in computer vision and deep learning techniques have facilitated notable progress in scene understanding, thereby assisting rescue teams in achieving precise damage assessment. In this paper, we present RescueNet, a meticulously curated high-resolution post-disaster dataset that includes detailed cl... | Papers with Code archive (Jul 2025) | 9.118 |
CKC-A01-019 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 19 | Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding | Rui Fan; Mohammud Junaid Bocus; Yilong Zhu; Jianhao Jiao; Li Wang; Fulong Ma; et al. | 8 | 2019-04-18 | 2,019 | 2019 IEEE Intelligent Vehicles Symposium (IV) | 1904.08582 | https://arxiv.org/abs/1904.08582 | https://arxiv.org/pdf/1904.08582 | https://huggingface.co/papers/1904.08582 | 10.1109/IVS.2019.8814000 | https://www.semanticscholar.org/paper/a228ce656599fe1413ed0671c260b5dad1190e3e | 561 | 31 | https://github.com/ruirangerfan/road_crack_detection_net | PwC archive – official | ruirangerfan/road_crack_detection_net | 6 | 1 | none/unknown | 2019-04-17 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Image Segmentation; Semantic Segmentation | Crack is one of the most common road distresses which may pose road safety hazards. Generally, crack detection is performed by either certified inspectors or structural engineers. This task is, however, time-consuming, subjective and labor-intensive. In this paper, we propose a novel road crack detection algorithm base... | Papers with Code archive (Jul 2025) | 9.104 | |
CKC-A01-020 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 20 | Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion | Ethan Weber; Hassan Kané | 2 | 2020-04-12 | 2,020 | arXiv.org | 2004.05525 | https://arxiv.org/abs/2004.05525 | https://arxiv.org/pdf/2004.05525 | https://huggingface.co/papers/2004.05525 | https://www.semanticscholar.org/paper/b90065e4c3b245371d8febd98adee9e8da7f039e | 132 | 17 | https://github.com/ethanweber/xview2 | PwC archive – official | ethanweber/xview2 | 81 | 25 | MIT | Jupyter Notebook | 2021-01-08 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | 2D Semantic Segmentation; Building Damage Assessment; Change Detection | Automatic change detection and disaster damage assessment are currently procedures requiring a huge amount of labor and manual work by satellite imagery analysts. In the occurrences of natural disasters, timely change detection can save lives. In this work, we report findings on problem framing, data processing and tra... | Papers with Code archive (Jul 2025) | 8.894 | |
CKC-A01-021 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 21 | Pothole Detection Based on Disparity Transformation and Road Surface Modeling | Rui Fan; Umar Ozgunalp; Brett Hosking; Ming Liu; Ioannis Pitas | 5 | 2019-08-02 | 2,019 | IEEE Transactions on Image Processing | 1908.00894 | https://arxiv.org/abs/1908.00894 | https://arxiv.org/pdf/1908.00894 | https://huggingface.co/papers/1908.00894 | 10.1109/TIP.2019.2933750 | https://www.semanticscholar.org/paper/3bce94f05ab18ab65219b9b1f95f89b544810658 | 239 | 19 | https://github.com/ruirangerfan/stereo_pothole_datasets | PwC archive – official | ruirangerfan/stereo_pothole_datasets | 24 | 8 | MIT | MATLAB | 2021-01-29 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Pothole detection is one of the most important tasks for road maintenance. Computer vision approaches are generally based on either 2D road image analysis or 3D road surface modeling. However, these two categories are always used independently. Furthermore, the pothole detection accuracy is still far from satisfactory.... | Papers with Code archive (Jul 2025) | 8.89 | |
CKC-A01-022 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 22 | BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images | Yu Shen; Sijie Zhu; Taojiannan Yang; Chen Chen; Delu Pan; Jianyu Chen; et al. | 8 | 2021-05-16 | 2,021 | IEEE Transactions on Geoscience and Remote Sensing | 2105.07364 | https://arxiv.org/abs/2105.07364 | https://arxiv.org/pdf/2105.07364 | https://huggingface.co/papers/2105.07364 | 10.1109/TGRS.2021.3080580 | https://www.semanticscholar.org/paper/013ec50cdd6524f78bb0f575f5b51a498c669fcb | 134 | 13 | https://github.com/ShaneShen/BDANet-Building-Damage-Assessment | PwC archive – official | ShaneShen/BDANet-Building-Damage-Assessment | 41 | 6 | none/unknown | Python | 2023-11-15 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | 2D Semantic Segmentation; Building Damage Assessment; Data Augmentation; Extracting Buildings In Remote Sensing Images | Fast and effective responses are required when a natural disaster (e.g., earthquake, hurricane, etc.) strikes. Building damage assessment from satellite imagery is critical before relief effort is deployed. With a pair of pre- and post-disaster satellite images, building damage assessment aims at predicting the extent ... | Papers with Code archive (Jul 2025) | 8.574 |
CKC-A01-023 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 23 | An Efficient and Scalable Deep Learning Approach for Road Damage Detection | Sadra Naddaf-sh; M-Mahdi Naddaf-Sh; Amir R. Kashani; Hassan Zargarzadeh | 4 | 2020-11-18 | 2,020 | 2020 IEEE International Conference on Big Data (Big Data) | 2011.09577 | https://arxiv.org/abs/2011.09577 | https://arxiv.org/pdf/2011.09577 | https://huggingface.co/papers/2011.09577 | 10.1109/BigData50022.2020.9377751 | https://www.semanticscholar.org/paper/c61bb2cf817f0f42361f3b735f3de693153fbb6c | 85 | 5 | https://github.com/mahdi65/roadDamageDetection2020 | PwC archive – official | mahdi65/roadDamageDetection2020 | 95 | 30 | Apache-2.0 | Python | 2022-03-26 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Data Augmentation; Image Augmentation; Object Detection; Road Damage Detection | Pavement condition evaluation is essential to time the preventative or rehabilitative actions and control distress propagation. Failing to conduct timely evaluations can lead to severe structural and financial loss of the infrastructure and complete reconstructions. Automated computer-aided surveying measures can provi... | Papers with Code archive (Jul 2025) | 8.537 |
CKC-A01-024 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 24 | Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysis | Vishal Mandal; Abdul Rashid Mussah; Yaw Adu-Gyamfi | 3 | 2020-10-21 | 2,020 | 2020 IEEE International Conference on Big Data (Big Data) | 2010.10681 | https://arxiv.org/abs/2010.10681 | https://arxiv.org/pdf/2010.10681 | https://huggingface.co/papers/2010.10681 | 10.1109/BigData50022.2020.9378047 | https://www.semanticscholar.org/paper/63982eae1a00f420fd6678444bf0c94c9e754c61 | 74 | 4 | https://github.com/titanmu/RoadCrackDetection | PwC archive – official | titanmu/RoadCrackDetection | 120 | 41 | none/unknown | Python | 2020-10-30 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Classification; Deep Learning; General Classification; Road Damage Detection | Automatic detection and classification of pavement distresses is critical in timely maintaining and rehabilitating pavement surfaces. With the evolution of deep learning and high performance computing, the feasibility of vision-based pavement defect assessments has significantly improved. In this study, the authors dep... | Papers with Code archive (Jul 2025) | 8.515 |
CKC-A01-025 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 25 | CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks | Shreyas Kulkarni; Shreyas Singh; Dhananjay Balakrishnan; Siddharth Sharma; Saipraneeth Devunuri; Sai Chowdeswara Rao Korlapati | 6 | 2022-08-27 | 2,022 | ECCV Workshops | 2208.13054 | https://arxiv.org/abs/2208.13054 | https://arxiv.org/pdf/2208.13054 | https://huggingface.co/papers/2208.13054 | 10.48550/arXiv.2208.13054 | https://www.semanticscholar.org/paper/57e265b253f98d1f81615cf6ed9186248b16aac8 | 88 | 8 | https://github.com/Dhananjay42/crackseg9k | PwC archive – official | Dhananjay42/crackseg9k | 76 | 7 | none/unknown | Python | 2024-06-27 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Crack Segmentation | The detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. The manual process of crack detection is time-consuming and subjective to the inspectors. Several researchers have tried tackling this problem using traditional Image Processing or learning-based techniques. Howeve... | Papers with Code archive (Jul 2025) | 8.461 |
CKC-A01-026 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 26 | Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network | Zhun Fan; Yuming Wu; Jiewei Lu; Wenji Li | 4 | 2018-02-01 | 2,018 | arXiv.org | 1802.02208 | https://arxiv.org/abs/1802.02208 | https://arxiv.org/pdf/1802.02208 | https://huggingface.co/papers/1802.02208 | https://www.semanticscholar.org/paper/8e407e56102db3e727b2fdb16b11b33dfdfdc6b4 | 268 | 23 | https://github.com/brijml/xcaliber-task | PwC archive – community implementation | brijml/xcaliber-task | 13 | 7 | none/unknown | Python | 2019-09-06 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Multi-Label Classification; MUlTI-LABEL-ClASSIFICATION; Structured Prediction | Automated pavement crack detection is a challenging task that has been researched for decades due to the complicated pavement conditions in real world. In this paper, a supervised method based on deep learning is proposed, which has the capability of dealing with different pavement conditions. Specifically, a convoluti... | Papers with Code archive (Jul 2025) | 8.414 | |
CKC-A01-027 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 27 | Topology-aware Mamba for Crack Segmentation in Structures | Xin Zuo; Yu Sheng; Jifeng Shen; Yongwei Shan | 4 | 2024-10-25 | 2,024 | Automation in Construction | 2410.19894 | https://arxiv.org/abs/2410.19894 | https://arxiv.org/pdf/2410.19894 | https://huggingface.co/papers/2410.19894 | 10.1016/j.autcon.2024.105845 | https://www.semanticscholar.org/paper/dc0b3ac9d7bb749fc28672e84cafe61e36b1d86e | 46 | 1 | https://github.com/shengyu27/CrackMamba | PwC archive – official | shengyu27/CrackMamba | 31 | 3 | none/unknown | Python | 2026-06-16 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Crack Segmentation; Decoder; Mamba; Retinal Vessel Segmentation; Segmentation | CrackMamba, a Mamba-based model, is designed for efficient and accurate crack segmentation for monitoring the structural health of infrastructure. Traditional Convolutional Neural Network (CNN) models struggle with limited receptive fields, and while Vision Transformers (ViT) improve segmentation accuracy, they are com... | Papers with Code archive (Jul 2025) | 8.383 |
CKC-A01-028 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 28 | Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images | Navjot Kaur; Cheng-Chun Lee; Ali Mostafavi; Ali Mahdavi-Amiri | 4 | 2022-08-03 | 2,022 | Comput. Aided Civ. Infrastructure Eng. | 2208.02205 | https://arxiv.org/abs/2208.02205 | https://arxiv.org/pdf/2208.02205 | https://huggingface.co/papers/2208.02205 | 10.1111/mice.12981 | https://www.semanticscholar.org/paper/14f3b91fd524d853aea082b322008411d555da5e | 109 | 8 | https://github.com/nka77/DAHiTra | PwC archive – official | nka77/DAHiTra | 39 | 8 | MIT | Jupyter Notebook | 2023-06-14 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Building Damage Assessment; Change Detection; Decision Making; Domain Adaptation | This paper presents \dahitra, a novel deep-learning model with hierarchical transformers to classify building damages based on satellite images in the aftermath of natural disasters. Satellite imagery provides real-time and high-coverage information and offers opportunities to inform large-scale post-disaster building ... | Papers with Code archive (Jul 2025) | 8.345 |
CKC-A01-029 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 29 | Local Intensity Order Transformation for Robust Curvilinear Object Segmentation | Tianyi Shi; Nicolas Boutry; Yongchao Xu; Thierry Géraud | 4 | 2022-02-25 | 2,022 | IEEE Transactions on Image Processing | 2202.12587 | https://arxiv.org/abs/2202.12587 | https://arxiv.org/pdf/2202.12587 | https://huggingface.co/papers/2202.12587 | 10.1109/TIP.2022.3155954 | https://www.semanticscholar.org/paper/b089a1c3cbd145bd798a3909bcd426081425c92b | 66 | 6 | https://github.com/TY-Shi/LIOT | PwC archive – official | TY-Shi/LIOT | 89 | 4 | MIT | Python | 2022-10-12 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Crack Segmentation; Object; Segmentation; Semantic Segmentation | Segmentation of curvilinear structures is important in many applications, such as retinal blood vessel segmentation for early detection of vessel diseases and pavement crack segmentation for road condition evaluation and maintenance. Currently, deep learning-based methods have achieved impressive performance on these t... | Papers with Code archive (Jul 2025) | 8.255 |
CKC-A01-030 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 30 | A Convolutional-Transformer Network for Crack Segmentation with Boundary Awareness | Huaqi Tao; Bingxi Liu; Jinqiang Cui; Hong Zhang | 4 | 2023-02-23 | 2,023 | International Conference on Information Photonics | 2302.11728 | https://arxiv.org/abs/2302.11728 | https://arxiv.org/pdf/2302.11728 | https://huggingface.co/papers/2302.11728 | 10.1109/ICIP49359.2023.10222276 | https://www.semanticscholar.org/paper/6b45c3b73aa87a5a73587da3c08167fd203116e6 | 89 | 8 | https://github.com/HqiTao/CT-crackseg | PwC archive – community implementation | HqiTao/CT-crackseg | 66 | 7 | MIT | Python | 2024-09-12 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Crack Segmentation; Decoder | Cracks play a crucial role in assessing the safety and durability of manufactured buildings. However, the long and sharp topological features and complex background of cracks make the task of crack segmentation extremely challenging. In this paper, we propose a novel convolutional-transformer network based on encoder-d... | Papers with Code archive (Jul 2025) | 8.102 |
CKC-A01-031 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 31 | Road Damage Detection and Classification with Detectron2 and Faster R-CNN | Vung Pham; Chau Pham; Tommy Dang | 3 | 2020-10-28 | 2,020 | 2020 IEEE International Conference on Big Data (Big Data) | 2010.15021 | https://arxiv.org/abs/2010.15021 | https://arxiv.org/pdf/2010.15021 | https://huggingface.co/papers/2010.15021 | 10.1109/BigData50022.2020.9378027 | https://www.semanticscholar.org/paper/ccd5f5cc97d09cc4b70ade4730a071607295b9fb | 122 | 6 | https://github.com/iDataVisualizationLab/roaddamagedetector | PwC archive – official | iDataVisualizationLab/roaddamagedetector | 13 | 8 | none/unknown | Jupyter Notebook | 2020-11-19 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | General Classification; Road Damage Detection | The road is vital for many aspects of life, and road maintenance is crucial for human safety. One of the critical tasks to allow timely repair of road damages is to quickly and efficiently detect and classify them. This work details the strategies and experiments evaluated for these tasks. Specifically, we evaluate Det... | Papers with Code archive (Jul 2025) | 7.932 |
CKC-A01-032 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 32 | UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via Adversarial Image Restoration | Nachuan Ma; Rui Fan; Lihua Xie | 3 | 2024-01-28 | 2,024 | IEEE transactions on intelligent transportation systems (Print) | 2401.15647 | https://arxiv.org/abs/2401.15647 | https://arxiv.org/pdf/2401.15647 | https://huggingface.co/papers/2401.15647 | 10.1109/TITS.2024.3398037 | https://www.semanticscholar.org/paper/e8b6be3e7009d54ab72299307cd32bcfad442b69 | 44 | 1 | https://github.com/NaChuaN666/UP-CrackNet | PwC archive – community implementation | NaChuaN666/UP-CrackNet | 24 | 3 | none/unknown | Python | 2025-12-03 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Crack Segmentation; Generative Adversarial Network; Image Restoration; Semantic Segmentation; Unsupervised Anomaly Detection | Over the past decade, automated methods have been developed to detect cracks more efficiently, accurately, and objectively, with the ultimate goal of replacing conventional manual visual inspection techniques. Among these methods, semantic segmentation algorithms have demonstrated promising results in pixel-wise crack ... | Papers with Code archive (Jul 2025) | 7.916 |
CKC-A01-033 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 33 | Road Damage Detection Based on Unsupervised Disparity Map Segmentation | Rui Fan; Ming Liu | 2 | 2019-10-11 | 2,019 | IEEE transactions on intelligent transportation systems (Print) | 1910.04988 | https://arxiv.org/abs/1910.04988 | https://arxiv.org/pdf/1910.04988 | https://huggingface.co/papers/1910.04988 | 10.1109/TITS.2019.2947206 | https://www.semanticscholar.org/paper/50fcd05978a3f8c8a3d499723984803667ad4512 | 112 | 8 | https://github.com/ruirangerfan/unsupervised_disparity_map_segmentation | PwC archive – official | ruirangerfan/unsupervised_disparity_map_segmentation | 15 | 7 | MIT | MATLAB | 2021-01-29 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Road Damage Detection | This paper presents a novel road damage detection algorithm based on unsupervised disparity map segmentation. Firstly, a disparity map is transformed by minimizing an energy function with respect to stereo rig roll angle and road disparity projection model. Instead of solving this energy minimization problem using non-... | Papers with Code archive (Jul 2025) | 7.914 |
CKC-A01-034 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 34 | We Learn Better Road Pothole Detection: from Attention Aggregation to Adversarial Domain Adaptation | Rui Fan; Hengli Wang; Mohammud J. Bocus; Ming Liu | 4 | 2020-08-16 | 2,020 | ECCV Workshops | 2008.06840 | https://arxiv.org/abs/2008.06840 | https://arxiv.org/pdf/2008.06840 | https://huggingface.co/papers/2008.06840 | 10.36227/techrxiv.12813323.v1 | https://www.semanticscholar.org/paper/f2280b3a79261af59801347fe27117b942dc272d | 70 | 11 | https://github.com/hlwang1124/AAFramework | PwC archive – official | hlwang1124/AAFramework | 31 | 5 | MIT | Python | 2024-10-25 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Domain Adaptation; Segmentation; Semantic Segmentation; Thermal Image Segmentation | Manual visual inspection performed by certified inspectors is still the main form of road pothole detection. This process is, however, not only tedious, time-consuming and costly, but also dangerous for the inspectors. Furthermore, the road pothole detection results are always subjective, because they depend entirely o... | Papers with Code archive (Jul 2025) | 7.796 |
CKC-A01-035 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 35 | DamageCAT: A Deep Learning Transformer Framework for Typology-Based Post-Disaster Building Damage Categorization | Yiming Xiao; Ali Mostafavi | 2 | 2025-04-15 | 2,025 | International Journal of Disaster Risk Reduction | 2504.11637 | https://arxiv.org/abs/2504.11637 | https://arxiv.org/pdf/2504.11637 | https://huggingface.co/papers/2504.11637 | 10.1016/j.ijdrr.2025.105704 | https://www.semanticscholar.org/paper/9fc6d12bde18154b1089b8cd20f0f412a98aeadf | 22 | 1 | https://github.com/YimingXiao98/DamageCAT | PwC archive – official | YimingXiao98/DamageCAT | 4 | 2 | Apache-2.0 | Python | 2025-06-04 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Building Damage Assessment; Disaster Response | Natural disasters increasingly threaten communities worldwide, creating an urgent need for rapid, reliable building damage assessment to guide emergency response and recovery efforts. Current methods typically classify damage in binary (damaged/undamaged) or ordinal severity terms, limiting their practical utility. In ... | Papers with Code archive (Jul 2025) | 7.74 |
CKC-A01-036 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 36 | MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos | Xiaoyu Zhu; Junwei Liang; Alexander Hauptmann | 3 | 2020-06-30 | 2,020 | IEEE Workshop/Winter Conference on Applications of Computer Vision | 2006.16479 | https://arxiv.org/abs/2006.16479 | https://arxiv.org/pdf/2006.16479 | https://huggingface.co/papers/2006.16479 | 10.1109/WACV48630.2021.00207 | https://www.semanticscholar.org/paper/5f30df0235dc06f69b51a8d52b7ddba09cad43a3 | 74 | 11 | https://github.com/zgzxy001/MSNET | PwC archive – community implementation | zgzxy001/MSNET | 41 | 4 | none/unknown | Python | 2022-06-04 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Instance Segmentation; Region Proposal; Semantic Segmentation | In this paper, we study the problem of efficiently assessing building damage after natural disasters like hurricanes, floods or fires, through aerial video analysis. We make two main contributions. The first contribution is a new dataset, consisting of user-generated aerial videos from social media with annotations of ... | Papers with Code archive (Jul 2025) | 7.686 |
CKC-A01-037 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 37 | Road Damage Detection using Deep Ensemble Learning | Keval Doshi; Yasin Yilmaz | 2 | 2020-10-30 | 2,020 | 2020 IEEE International Conference on Big Data (Big Data) | 2011.00728 | https://arxiv.org/abs/2011.00728 | https://arxiv.org/pdf/2011.00728 | https://huggingface.co/papers/2011.00728 | 10.1109/BigData50022.2020.9377774 | https://www.semanticscholar.org/paper/3c7a2b378563c1783724c10f299183b190db0272 | 67 | 7 | https://github.com/kevaldoshi17/IEEE-Big-Data-2020 | PwC archive – official | kevaldoshi17/IEEE-Big-Data-2020 | 25 | 10 | MIT | Jupyter Notebook | 2023-05-29 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Ensemble Learning; Management; Road Damage Detection | Road damage detection is critical for the maintenance of a road, which traditionally has been performed using expensive high-performance sensors. With the recent advances in technology, especially in computer vision, it is now possible to detect and categorize different types of road damages, which can facilitate effic... | Papers with Code archive (Jul 2025) | 7.649 |
CKC-A01-038 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 38 | Semi-Supervised Confidence-Level-based Contrastive Discrimination for Class-Imbalanced Semantic Segmentation | Kangcheng Liu | 1 | 2022-11-28 | 2,022 | Cyber .. | 2211.15066 | https://arxiv.org/abs/2211.15066 | https://arxiv.org/pdf/2211.15066 | https://huggingface.co/papers/2211.15066 | 10.1109/CYBER55403.2022.9907146 | https://www.semanticscholar.org/paper/cb95c47e4b5a2d0206cd7067887d3e93ee3a3ffd | 18 | 0 | https://github.com/KangchengLiu/Crack-Detection-and-Segmentation-Dataset-for-UAV-Inspection | PwC archive – official | KangchengLiu/Crack-Detection-and-Segmentation-Dataset-for-UAV-Inspection | 272 | 33 | MIT | Python | 2023-02-17 | false | 2026-10-16 | 2 | related | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Contrastive Learning; Road Segmentation; Segmentation; Semantic Segmentation | To overcome the data-hungry challenge, we have proposed a semi-supervised contrastive learning framework for the task of class-imbalanced semantic segmentation. First and foremost, to make the model operate in a semi-supervised manner, we proposed the confidence-level-based contrastive learning to achieve instance disc... | Papers with Code archive (Jul 2025) | 7.549 |
CKC-A01-039 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 39 | DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection | Vitjan Zavrtanik; Matej Kristan; Danijel Skočaj | 3 | 2021-08-17 | 2,021 | IEEE International Conference on Computer Vision | 2108.07610 | https://arxiv.org/abs/2108.07610 | https://arxiv.org/pdf/2108.07610 | https://huggingface.co/papers/2108.07610 | 10.1109/ICCV48922.2021.00822 | https://www.semanticscholar.org/paper/95a26eafabf06b1fc5dec6c460a927cf5964e97e | 1,307 | 237 | https://github.com/VitjanZ/DRAEM | PwC archive – official | VitjanZ/DRAEM | 297 | 71 | MIT | Python | 2023-01-02 | false | 2026-10-16 | 3 | foundation / supporting | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Classification; Anomaly Detection; Anomaly Localization; Anomaly Segmentation; Defect Detection; Unsupervised Anomaly Detection | Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance. Recent surface anomaly detection methods rely on generative models to accurately reconstruct the normal areas and to fail on anomalies. These methods are trained only on anomaly-free images, and often ... | Papers with Code archive (Jul 2025) | 10.325 |
CKC-A01-040 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 40 | CutPaste: Self-Supervised Learning for Anomaly Detection and Localization | Chun-Liang Li; Kihyuk Sohn; Jinsung Yoon; Tomas Pfister | 4 | 2021-04-08 | 2,021 | Computer Vision and Pattern Recognition | 2104.04015 | https://arxiv.org/abs/2104.04015 | https://arxiv.org/pdf/2104.04015 | https://huggingface.co/papers/2104.04015 | 10.1109/CVPR46437.2021.00954 | https://www.semanticscholar.org/paper/78d80c343d36baaf89f18e12d325cf6309fb6c8f | 1,325 | 184 | https://github.com/LilitYolyan/CutPaste | PwC archive – community implementation | LilitYolyan/CutPaste | 133 | 28 | MIT | Python | 2022-04-21 | false | 2026-10-16 | 3 | foundation / supporting | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Classification; Anomaly Detection; Data Augmentation; Defect Detection; One-class classifier; Self-Supervised Learning; Transfer Learning; Unsupervised Anomaly Detection | We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. To this end, we propose a two-stage framework for building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then buil... | Papers with Code archive (Jul 2025) | 9.639 |
CKC-A01-041 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 41 | Mixed supervision for surface-defect detection: from weakly to fully supervised learning | Jakob Božič; Domen Tabernik; Danijel Skočaj | 3 | 2021-04-13 | 2,021 | Computers in industry (Print) | 2104.06064 | https://arxiv.org/abs/2104.06064 | https://arxiv.org/pdf/2104.06064 | https://huggingface.co/papers/2104.06064 | 10.1016/j.compind.2021.103459 | https://www.semanticscholar.org/paper/7eb24c2109f75c614cc7aa4c1cac8b643c05e70c | 416 | 42 | https://github.com/vicoslab/mixed-segdec-net-comind2021 | PwC archive – official | vicoslab/mixed-segdec-net-comind2021 | 319 | 85 | NOASSERTION | Python | 2022-03-17 | false | 2026-10-16 | 3 | foundation / supporting | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Defect Detection; Supervised Defect Detection; Weakly Supervised Defect Detection | Deep-learning methods have recently started being employed for addressing surface-defect detection problems in industrial quality control. However, with a large amount of data needed for learning, often requiring high-precision labels, many industrial problems cannot be easily solved, or the cost of the solutions would... | Papers with Code archive (Jul 2025) | 9.217 |
CKC-A01-042 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 42 | A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization | Qiyu Chen; Huiyuan Luo; Chengkan Lv; Zhengtao Zhang | 4 | 2024-07-12 | 2,024 | European Conference on Computer Vision | 2407.09359 | https://arxiv.org/abs/2407.09359 | https://arxiv.org/pdf/2407.09359 | https://huggingface.co/papers/2407.09359 | 10.48550/arXiv.2407.09359 | https://www.semanticscholar.org/paper/69bcc8a20663b664df102fd0aad4a1bbbfc93589 | 136 | 20 | https://github.com/cqylunlun/GLASS | PwC archive – official | cqylunlun/GLASS | 397 | 69 | MIT | Python | 2026-03-30 | false | 2026-10-16 | 3 | foundation / supporting | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Defect Detection; Unsupervised Anomaly Detection | Anomaly synthesis strategies can effectively enhance unsupervised anomaly detection. However, existing strategies have limitations in the coverage and controllability of anomaly synthesis, particularly for weak defects that are very similar to normal regions. In this paper, we propose Global and Local Anomaly co-Synthe... | Papers with Code archive (Jul 2025) | 9.213 |
CKC-A01-043 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 43 | Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows | Marco Rudolph; Bastian Wandt; Bodo Rosenhahn | 3 | 2020-08-28 | 2,020 | IEEE Workshop/Winter Conference on Applications of Computer Vision | 2008.12577 | https://arxiv.org/abs/2008.12577 | https://arxiv.org/pdf/2008.12577 | https://huggingface.co/papers/2008.12577 | 10.1109/WACV48630.2021.00195 | https://www.semanticscholar.org/paper/b1464ca857593c049873421db2f37bf2d0ff676d | 469 | 55 | https://github.com/marco-rudolph/differnet | PwC archive – official | marco-rudolph/differnet | 230 | 69 | none/unknown | Python | 2023-03-21 | false | 2026-10-16 | 3 | foundation / supporting | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Defect Detection; Supervised Defect Detection | The detection of manufacturing errors is crucial in fabrication processes to ensure product quality and safety standards. Since many defects occur very rarely and their characteristics are mostly unknown a priori, their detection is still an open research question. To this end, we propose DifferNet: It leverages the de... | Papers with Code archive (Jul 2025) | 9.174 |
CKC-A01-044 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 44 | Online PCB Defect Detector On A New PCB Defect Dataset | Sanli Tang; Fan He; Xiaolin Huang; Jie Yang | 4 | 2019-02-17 | 2,019 | arXiv.org | 1902.06197 | https://arxiv.org/abs/1902.06197 | https://arxiv.org/pdf/1902.06197 | https://huggingface.co/papers/1902.06197 | https://www.semanticscholar.org/paper/5401005e8ae0f3e566b950ed184d009d8d1f2166 | 206 | 20 | https://github.com/tangsanli5201/DeepPCB | PwC archive – official | tangsanli5201/DeepPCB | 557 | 119 | MIT | Python | 2018-12-19 | false | 2026-10-16 | 3 | foundation / supporting | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Defect Detection | Previous works for PCB defect detection based on image difference and image processing techniques have already achieved promising performance. However, they sometimes fall short because of the unaccounted defect patterns or over-sensitivity about some hyper-parameters. In this work, we design a deep model that accurate... | Papers with Code archive (Jul 2025) | 8.795 | |
CKC-A01-045 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 45 | Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection | Marco Rudolph; Tom Wehrbein; Bodo Rosenhahn; Bastian Wandt | 4 | 2021-10-06 | 2,021 | IEEE Workshop/Winter Conference on Applications of Computer Vision | 2110.02855 | https://arxiv.org/abs/2110.02855 | https://arxiv.org/pdf/2110.02855 | https://huggingface.co/papers/2110.02855 | 10.1109/WACV51458.2022.00189 | https://www.semanticscholar.org/paper/5b6b1f438ba40e04a4e3c1e02bd14fe2e66167f9 | 348 | 32 | https://github.com/marco-rudolph/cs-flow | PwC archive – official | marco-rudolph/cs-flow | 120 | 31 | none/unknown | Python | 2023-03-16 | false | 2026-10-16 | 3 | foundation / supporting | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Defect Detection; Unsupervised Anomaly Detection | In industrial manufacturing processes, errors frequently occur at unpredictable times and in unknown manifestations. We tackle the problem of automatic defect detection without requiring any image samples of defective parts. Recent works model the distribution of defect-free image data, using either strong statistical ... | Papers with Code archive (Jul 2025) | 8.553 |
CKC-A01-046 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 46 | Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection | Oliver Rippel; Patrick Mertens; Dorit Merhof | 3 | 2020-05-28 | 2,020 | International Conference on Pattern Recognition | 2005.14140 | https://arxiv.org/abs/2005.14140 | https://arxiv.org/pdf/2005.14140 | https://huggingface.co/papers/2005.14140 | 10.1109/ICPR48806.2021.9412109 | https://www.semanticscholar.org/paper/37595f7a51982d776e57c7280b9445474d90f0be | 302 | 25 | https://github.com/ORippler/gaussian-ad-mvtec | PwC archive – official | ORippler/gaussian-ad-mvtec | 104 | 17 | AGPL-3.0 | Python | 2022-11-25 | true | 2026-10-16 | 3 | foundation / supporting | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Transfer Learning | Anomaly Detection (AD) in images is a fundamental computer vision problem and refers to identifying images and image substructures that deviate significantly from the norm. Popular AD algorithms commonly try to learn a model of normality from scratch using task specific datasets, but are limited to semi-supervised appr... | Papers with Code archive (Jul 2025) | 8.341 |
CKC-A01-047 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 47 | Asymmetric Student-Teacher Networks for Industrial Anomaly Detection | Marco Rudolph; Tom Wehrbein; Bodo Rosenhahn; Bastian Wandt | 4 | 2022-10-14 | 2,022 | IEEE Workshop/Winter Conference on Applications of Computer Vision | 2210.07829 | https://arxiv.org/abs/2210.07829 | https://arxiv.org/pdf/2210.07829 | https://huggingface.co/papers/2210.07829 | 10.1109/WACV56688.2023.00262 | https://www.semanticscholar.org/paper/655158947fd2a3b1e77703d9f4e951e7c583f894 | 278 | 38 | https://github.com/marco-rudolph/AST | PwC archive – official | marco-rudolph/AST | 84 | 11 | none/unknown | Python | 2023-02-28 | false | 2026-10-16 | 3 | foundation / supporting | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | 3D Anomaly Detection; Anomaly Detection; Defect Detection; Density Estimation; RGB+3D Anomaly Detection and Segmentation | Industrial defect detection is commonly addressed with anomaly detection (AD) methods where no or only incomplete data of potentially occurring defects is available. This work discovers previously unknown problems of student-teacher approaches for AD and proposes a solution, where two neural networks are trained to pro... | Papers with Code archive (Jul 2025) | 8.153 |
CKC-A01-048 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 48 | Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging | Shansong Wang; Mojtaba Safari; Qiang Li; Chih-Wei Chang; Richard LJ Qiu; Justin Roper; et al. | 8 | 2025-02-19 | 2,025 | Research Square | 2502.14064 | https://arxiv.org/abs/2502.14064 | https://arxiv.org/pdf/2502.14064 | https://huggingface.co/papers/2502.14064 | 10.21203/rs.3.rs-6129856/v1 | https://www.semanticscholar.org/paper/1c6fe1a40c41702f8449e7f7340ce41b631b70b6 | 40 | 4 | https://github.com/wangshansong1/Triad | PwC archive – official | wangshansong1/Triad | 69 | 3 | none/unknown | Python | 2026-03-10 | false | 2026-10-16 | 3 | foundation / supporting | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Cancer Classification; Computed Tomography (CT); Image Registration; Medical Image Registration; Tumor Segmentation | Vision foundation models (VFMs) are pre-trained on extensive image datasets to learn general representations for diverse types of data. These models can subsequently be fine-tuned for specific downstream tasks, significantly boosting performance across a broad range of applications. However, existing vision foundation ... | Papers with Code archive (Jul 2025) | 8.138 |
CKC-A01-049 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 49 | Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection | Choubo Ding; Guansong Pang; Chunhua Shen | 3 | 2022-03-28 | 2,022 | Computer Vision and Pattern Recognition | 2203.14506 | https://arxiv.org/abs/2203.14506 | https://arxiv.org/pdf/2203.14506 | https://huggingface.co/papers/2203.14506 | 10.1109/CVPR52688.2022.00724 | https://www.semanticscholar.org/paper/6a4f7514cf25a36b746b09eab4a2576a12961cb0 | 213 | 36 | https://github.com/Choubo/DRA | PwC archive – official | Choubo/DRA | 94 | 10 | AGPL-3.0 | Python | 2022-04-11 | false | 2026-10-16 | 3 | foundation / supporting | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Supervised Anomaly Detection; Supervised Defect Detection | Despite most existing anomaly detection studies assume the availability of normal training samples only, a few labeled anomaly examples are often available in many real-world applications, such as defect samples identified during random quality inspection, lesion images confirmed by radiologists in daily medical screen... | Papers with Code archive (Jul 2025) | 7.943 |
CKC-A01-050 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 50 | ScratchDet: Training Single-Shot Object Detectors from Scratch | Rui Zhu; Shifeng Zhang; Xiaobo Wang; Longyin Wen; Hailin Shi; Liefeng Bo; et al. | 7 | 2018-10-19 | 2,018 | scratchdet-training-single-shot-object | 1810.08425 | https://arxiv.org/abs/1810.08425 | https://arxiv.org/pdf/1810.08425 | https://huggingface.co/papers/1810.08425 | 10.1109/CVPR.2019.00237 | https://www.semanticscholar.org/paper/eaa639c7af9a27a000b4c7675f5bee0755970427 | 136 | 12 | https://github.com/KimSoybean/ScratchDet | PwC archive – official | KimSoybean/ScratchDet | 226 | 36 | none/unknown | Python | 2019-06-16 | false | 2026-10-16 | 3 | foundation / supporting | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | General Classification; Object; object-detection; Object Detection | Current state-of-the-art object objectors are fine-tuned from the off-the-shelf networks pretrained on large-scale classification dataset ImageNet, which incurs some additional problems: 1) The classification and detection have different degrees of sensitivity to translation, resulting in the learning objective bias; 2... | Papers with Code archive (Jul 2025) | 7.932 |
CKC-A01-051 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 51 | DSR -- A dual subspace re-projection network for surface anomaly detection | Vitjan Zavrtanik; Matej Kristan; Danijel Skočaj | 3 | 2022-08-02 | 2,022 | European Conference on Computer Vision | 2208.01521 | https://arxiv.org/abs/2208.01521 | https://arxiv.org/pdf/2208.01521 | https://huggingface.co/papers/2208.01521 | 10.48550/arXiv.2208.01521 | https://www.semanticscholar.org/paper/a6a237a7625f2c3175e44070ee9bad6e917798b0 | 235 | 19 | https://github.com/VitjanZ/DSR_anomaly_detection | PwC archive – official | VitjanZ/DSR_anomaly_detection | 57 | 10 | Apache-2.0 | Python | 2022-07-18 | false | 2026-10-16 | 3 | foundation / supporting | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Anomaly Localization; Defect Detection; Supervised Defect Detection; Unsupervised Anomaly Detection; Weakly Supervised Defect Detection | The state-of-the-art in discriminative unsupervised surface anomaly detection relies on external datasets for synthesizing anomaly-augmented training images. Such approaches are prone to failure on near-in-distribution anomalies since these are difficult to be synthesized realistically due to their similarity to anomal... | Papers with Code archive (Jul 2025) | 7.794 |
CKC-A01-052 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 52 | MINet: Multi-scale Interactive Network for Real-time Salient Object Detection of Strip Steel Surface Defects | Kunye Shen; Xiaofei Zhou; Zhi Liu | 3 | 2024-05-25 | 2,024 | IEEE Transactions on Industrial Informatics | 2405.16096 | https://arxiv.org/abs/2405.16096 | https://arxiv.org/pdf/2405.16096 | https://huggingface.co/papers/2405.16096 | 10.1109/TII.2024.3366221 | https://www.semanticscholar.org/paper/057bb5c2787eba18281175ee8f1364279fd803bd | 115 | 6 | https://github.com/Kunye-Shen/MINet | PwC archive – official | Kunye-Shen/MINet | 28 | 4 | none/unknown | Python | 2024-06-04 | false | 2026-10-16 | 3 | foundation / supporting | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | CPU; Defect Detection; GPU; object-detection; Object Detection; Salient Object Detection | The automated surface defect detection is a fundamental task in industrial production, and the existing saliencybased works overcome the challenging scenes and give promising detection results. However, the cutting-edge efforts often suffer from large parameter size, heavy computational cost, and slow inference speed, ... | Papers with Code archive (Jul 2025) | 7.737 |
CKC-A01-053 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 53 | Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders | Paul Bergmann; Sindy Löwe; Michael Fauser; David Sattlegger; Carsten Steger | 5 | 2018-07-05 | 2,018 | VISIGRAPP | 1807.02011 | https://arxiv.org/abs/1807.02011 | https://arxiv.org/pdf/1807.02011 | https://huggingface.co/papers/1807.02011 | 10.5220/0007364503720380 | https://www.semanticscholar.org/paper/9c24454b071bc8e96ea46c5064a7bddf07cca464 | 851 | 104 | https://github.com/miriskalt/AnomalyDetectionAutoencoder | PwC archive – community implementation | miriskalt/AnomalyDetectionAutoencoder | 6 | 0 | none/unknown | Python | 2021-06-24 | false | 2026-10-16 | 3 | foundation / supporting | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Segmentation | Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data. Most commonly, this task is performed by thresholding a pixel-wise reconstruction error based on an $\ell^p$ distance. This procedure, however, leads to large residuals whenever the reconstruction encompasses ... | Papers with Code archive (Jul 2025) | 7.721 |
CKC-A01-054 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 54 | Towards Total Recall in Industrial Anomaly Detection | Karsten Roth; Latha Pemula; Joaquin Zepeda; Bernhard Schölkopf; Thomas Brox; Peter Gehler | 6 | 2021-06-15 | 2,021 | Computer Vision and Pattern Recognition | 2106.08265 | https://arxiv.org/abs/2106.08265 | https://arxiv.org/pdf/2106.08265 | https://huggingface.co/papers/2106.08265 | 10.1109/CVPR52688.2022.01392 | https://www.semanticscholar.org/paper/23ad8fc48530ce366f8192dfb48d0f7df1dba277 | 1,953 | 443 | https://github.com/amazon-science/patchcore-inspection | PwC archive – official | amazon-science/patchcore-inspection | 1,415 | 257 | Apache-2.0 | Python | 2024-07-10 | false | 2026-10-16 | 4 | transferable method (industrial anomaly) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | 3D Anomaly Detection; Anomaly Classification; Anomaly Detection; Anomaly Segmentation; Outlier Detection; Unsupervised Anomaly Detection | Being able to spot defective parts is a critical component in large-scale industrial manufacturing. A particular challenge that we address in this work is the cold-start problem: fit a model using nominal (non-defective) example images only. While handcrafted solutions per class are possible, the goal is to build syste... | Papers with Code archive (Jul 2025) | 11.505 |
CKC-A01-055 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 55 | Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery | Thomas Schlegl; Philipp Seeböck; Sebastian M. Waldstein; Ursula Schmidt-Erfurth; Georg Langs | 5 | 2017-03-17 | 2,017 | Information Processing in Medical Imaging | 1703.05921 | https://arxiv.org/abs/1703.05921 | https://arxiv.org/pdf/1703.05921 | https://huggingface.co/papers/1703.05921 | 10.1007/978-3-319-59050-9_12 | https://www.semanticscholar.org/paper/e163a2e89c136cb4442e34c72f7173a0ff46dc79 | 2,571 | 236 | https://github.com/LeeDoYup/AnoGAN-tf | PwC archive – official | LeeDoYup/AnoGAN-tf | 281 | 88 | MIT | Python | 2019-04-16 | false | 2026-10-16 | 4 | transferable method (industrial anomaly) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Generative Adversarial Network; Unsupervised Anomaly Detection | Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection. High annotation effort and the limitation to a vocabulary of known markers ... | Papers with Code archive (Jul 2025) | 10.973 |
CKC-A01-056 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 56 | EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies | Kilian Batzner; Lars Heckler; Rebecca König | 3 | 2023-03-25 | 2,023 | IEEE Workshop/Winter Conference on Applications of Computer Vision | 2303.14535 | https://arxiv.org/abs/2303.14535 | https://arxiv.org/pdf/2303.14535 | https://huggingface.co/papers/2303.14535 | 10.1109/WACV57701.2024.00020 | https://www.semanticscholar.org/paper/a5a1486a7874c85fb62c700594db390976a47632 | 449 | 58 | https://github.com/open-edge-platform/anomalib | PwC archive – community implementation | open-edge-platform/anomalib | 6,253 | 1,004 | Apache-2.0 | Python | 2026-10-15 | false | 2026-10-16 | 4 | transferable method (industrial anomaly) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Computational Efficiency; GPU; Semi-supervised Anomaly Detection; Unsupervised Anomaly Detection | Detecting anomalies in images is an important task, especially in real-time computer vision applications. In this work, we focus on computational efficiency and propose a lightweight feature extractor that processes an image in less than a millisecond on a modern GPU. We then use a student-teacher approach to detect an... | Papers with Code archive (Jul 2025) | 10.48 |
CKC-A01-057 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 57 | AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP | Wenxin Ma; Xu Zhang; Qingsong Yao; Fenghe Tang; Chenxu Wu; Yingtai Li; et al. | 9 | 2025-03-09 | 2,025 | Computer Vision and Pattern Recognition | 2503.06661 | https://arxiv.org/abs/2503.06661 | https://arxiv.org/pdf/2503.06661 | https://huggingface.co/papers/2503.06661 | 10.1109/CVPR52734.2025.00447 | https://www.semanticscholar.org/paper/7592c30553798b4477351a56261bf879881c24f8 | 153 | 16 | https://github.com/Mwxinnn/AA-CLIP | PwC archive – official | Mwxinnn/AA-CLIP | 272 | 23 | Apache-2.0 | Python | 2025-05-26 | false | 2026-10-16 | 4 | transferable method (industrial anomaly) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Anomaly Localization; Lesion Detection; zero-shot anomaly detection | Anomaly detection (AD) identifies outliers for applications like defect and lesion detection. While CLIP shows promise for zero-shot AD tasks due to its strong generalization capabilities, its inherent Anomaly-Unawareness leads to limited discrimination between normal and abnormal features. To address this problem, we ... | Papers with Code archive (Jul 2025) | 10.142 |
CKC-A01-058 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 58 | SimpleNet: A Simple Network for Image Anomaly Detection and Localization | Zhikang Liu; Yiming Zhou; Yuansheng Xu; Zilei Wang | 4 | 2023-03-27 | 2,023 | Computer Vision and Pattern Recognition | 2303.15140 | https://arxiv.org/abs/2303.15140 | https://arxiv.org/pdf/2303.15140 | https://huggingface.co/papers/2303.15140 | 10.1109/CVPR52729.2023.01954 | https://www.semanticscholar.org/paper/a23c0a89bd21bd2481fedcdd6d1ac891c6c06bdc | 694 | 91 | https://github.com/DonaldRR/SimpleNet | PwC archive – official | DonaldRR/SimpleNet | 603 | 101 | MIT | Python | 2024-08-12 | false | 2026-10-16 | 4 | transferable method (industrial anomaly) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Classification; Anomaly Detection; Anomaly Segmentation; GPU; Novelty Detection | We propose a simple and application-friendly network (called SimpleNet) for detecting and localizing anomalies. SimpleNet consists of four components: (1) a pre-trained Feature Extractor that generates local features, (2) a shallow Feature Adapter that transfers local features towards target domain, (3) a simple Anomal... | Papers with Code archive (Jul 2025) | 10.046 |
CKC-A01-059 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 59 | Anomalib: A Deep Learning Library for Anomaly Detection | Samet Akcay; Dick Ameln; Ashwin Vaidya; Barath Lakshmanan; Nilesh Ahuja; Utku Genc | 6 | 2022-02-16 | 2,022 | International Conference on Information Photonics | 2202.08341 | https://arxiv.org/abs/2202.08341 | https://arxiv.org/pdf/2202.08341 | https://huggingface.co/papers/2202.08341 | 10.1109/ICIP46576.2022.9897283 | https://www.semanticscholar.org/paper/9cd70ca81b4653aa2048002273c5ac9c36641390 | 182 | 19 | https://github.com/open-edge-platform/anomalib | PwC archive – official | open-edge-platform/anomalib | 6,253 | 1,004 | Apache-2.0 | Python | 2026-10-15 | false | 2026-10-16 | 4 | transferable method (industrial anomaly) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Deep Learning; Model Optimization; Unsupervised Anomaly Detection | This paper introduces anomalib, a novel library for unsupervised anomaly detection and localization. With reproducibility and modularity in mind, this open-source library provides algorithms from the literature and a set of tools to design custom anomaly detection algorithms via a plug-and-play approach. Anomalib compr... | Papers with Code archive (Jul 2025) | 9.88 |
CKC-A01-060 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 60 | AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection | Yunkang Cao; Jiangning Zhang; Luca Frittoli; Yuqi Cheng; Weiming Shen; Giacomo Boracchi | 6 | 2024-07-22 | 2,024 | European Conference on Computer Vision | 2407.15795 | https://arxiv.org/abs/2407.15795 | https://arxiv.org/pdf/2407.15795 | https://huggingface.co/papers/2407.15795 | 10.1007/978-3-031-72761-0_4 | https://www.semanticscholar.org/paper/06cb9838ae2e386360aaacbf50775e164a741bd9 | 278 | 43 | https://github.com/caoyunkang/AdaCLIP | PwC archive – official | caoyunkang/AdaCLIP | 313 | 19 | MIT | Python | 2025-07-07 | false | 2026-10-16 | 4 | transferable method (industrial anomaly) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Language Modeling; Language Modelling; zero-shot anomaly detection | Zero-shot anomaly detection (ZSAD) targets the identification of anomalies within images from arbitrary novel categories. This study introduces AdaCLIP for the ZSAD task, leveraging a pre-trained vision-language model (VLM), CLIP. AdaCLIP incorporates learnable prompts into CLIP and optimizes them through training on a... | Papers with Code archive (Jul 2025) | 9.806 |
CKC-A01-061 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 61 | Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection | Wei Luo; Yunkang Cao; Haiming Yao; Xiaotian Zhang; Jianan Lou; Yuqi Cheng; et al. | 8 | 2025-03-04 | 2,025 | Computer Vision and Pattern Recognition | 2503.02424 | https://arxiv.org/abs/2503.02424 | https://arxiv.org/pdf/2503.02424 | https://huggingface.co/papers/2503.02424 | 10.1109/CVPR52734.2025.00932 | https://www.semanticscholar.org/paper/bb7d5504c9a98d887a8506ba4a9a7cf439c90ba3 | 99 | 15 | https://github.com/luow23/INP-Former | HF Papers – linked repo | luow23/INP-Former | 323 | 42 | MIT | Python | 2026-05-26 | false | 2026-10-16 | 4 | transferable method (industrial anomaly) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly detection (AD) is essential for industrial inspection, yet existing methods typically rely on ``comparing'' test images to normal references from a training set. However, variations in appearance and positioning often complicate the alignment of these references with the test image, limiting detection accuracy.... | HF Papers | 9.796 | |
CKC-A01-062 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 62 | Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection | Jia Guo; Shuai Lu; Weihang Zhang; Fang Chen; Hongen Liao; Huiqi Li | 6 | 2024-05-23 | 2,024 | Computer Vision and Pattern Recognition | 2405.14325 | https://arxiv.org/abs/2405.14325 | https://arxiv.org/pdf/2405.14325 | https://huggingface.co/papers/2405.14325 | 10.1109/CVPR52734.2025.01900 | https://www.semanticscholar.org/paper/063e7e1deb2fd68a3701b36b4ebc2b651d5983ae | 177 | 40 | https://github.com/guojiajeremy/Dinomaly | PwC archive – official | guojiajeremy/Dinomaly | 537 | 63 | Apache-2.0 | Python | 2026-09-17 | false | 2026-10-16 | 4 | transferable method (industrial anomaly) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Multi-class Anomaly Detection; Philosophy; Unsupervised Anomaly Detection | Recent studies highlighted a practical setting of unsupervised anomaly detection (UAD) that builds a unified model for multi-class images. Despite various advancements addressing this challenging task, the detection performance under the multi-class setting still lags far behind state-of-the-art class-separated models.... | Papers with Code archive (Jul 2025) | 9.626 |
CKC-A01-063 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 63 | Student-Teacher Feature Pyramid Matching for Anomaly Detection | Guodong Wang; Shumin Han; Errui Ding; Di Huang | 4 | 2021-03-07 | 2,021 | British Machine Vision Conference | 2103.04257 | https://arxiv.org/abs/2103.04257 | https://arxiv.org/pdf/2103.04257 | https://huggingface.co/papers/2103.04257 | 10.5244/c.35.349 | https://www.semanticscholar.org/paper/02805f18989b7e77f30ee13defd6fecfcd0f499f | 397 | 54 | https://github.com/open-edge-platform/geti | PwC archive – community implementation | open-edge-platform/geti | 1,356 | 481 | Apache-2.0 | Python | 2026-10-16 | false | 2026-10-16 | 4 | transferable method (industrial anomaly) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; image-classification; Image Classification; Unsupervised Anomaly Detection | Anomaly detection is a challenging task and usually formulated as an one-class learning problem for the unexpectedness of anomalies. This paper proposes a simple yet powerful approach to this issue, which is implemented in the student-teacher framework for its advantages but substantially extends it in terms of both ac... | Papers with Code archive (Jul 2025) | 9.593 |
CKC-A01-064 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 64 | Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings | Paul Bergmann; Michael Fauser; David Sattlegger; Carsten Steger | 4 | 2019-11-06 | 2,019 | uninformed-students-student-teacher-anomaly-1 | 1911.02357 | https://arxiv.org/abs/1911.02357 | https://arxiv.org/pdf/1911.02357 | https://huggingface.co/papers/1911.02357 | 10.1109/cvpr42600.2020.00424 | https://www.semanticscholar.org/paper/41747cbdbed84762dfbfc305254c97021279dc6e | 1,028 | 131 | https://github.com/denguir/student-teacher-anomaly-detection | PwC archive – community implementation | denguir/student-teacher-anomaly-detection | 186 | 46 | none/unknown | Python | 2024-11-25 | false | 2026-10-16 | 4 | transferable method (industrial anomaly) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Anomaly Segmentation; Descriptive; Segmentation; Unsupervised Anomaly Detection | We introduce a powerful student-teacher framework for the challenging problem of unsupervised anomaly detection and pixel-precise anomaly segmentation in high-resolution images. Student networks are trained to regress the output of a descriptive teacher network that was pretrained on a large dataset of patches from nat... | Papers with Code archive (Jul 2025) | 9.552 |
CKC-A01-065 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 65 | Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation | Jihun Yi; Sungroh Yoon | 2 | 2020-06-29 | 2,020 | Asian Conference on Computer Vision | 2006.16067 | https://arxiv.org/abs/2006.16067 | https://arxiv.org/pdf/2006.16067 | https://huggingface.co/papers/2006.16067 | 10.1007/978-3-030-69544-6_23 | https://www.semanticscholar.org/paper/62b77e5cb85fc61b84edd532f6d65714be152596 | 544 | 66 | https://github.com/nuclearboy95/Anomaly-Detection-PatchSVDD-PyTorch | PwC archive – official | nuclearboy95/Anomaly-Detection-PatchSVDD-PyTorch | 253 | 53 | none/unknown | Python | 2020-10-19 | false | 2026-10-16 | 4 | transferable method (industrial anomaly) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Anomaly Segmentation; Segmentation; Self-Supervised Learning | In this paper, we address the problem of image anomaly detection and segmentation. Anomaly detection involves making a binary decision as to whether an input image contains an anomaly, and anomaly segmentation aims to locate the anomaly on the pixel level. Support vector data description (SVDD) is a long-standing algor... | Papers with Code archive (Jul 2025) | 9.369 |
CKC-A01-066 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 66 | PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection | Xiaofan Li; Zhizhong Zhang; Xin Tan; Chengwei Chen; Yanyun Qu; Yuan Xie; et al. | 7 | 2024-04-08 | 2,024 | Computer Vision and Pattern Recognition | 2404.05231 | https://arxiv.org/abs/2404.05231 | https://arxiv.org/pdf/2404.05231 | https://huggingface.co/papers/2404.05231 | 10.1109/CVPR52733.2024.01594 | https://www.semanticscholar.org/paper/5d629530b553de452405eb958820b0bffbba85d6 | 212 | 32 | https://github.com/FuNz-0/PromptAD | PwC archive – official | FuNz-0/PromptAD | 213 | 20 | Unlicense | Python | 2024-09-01 | false | 2026-10-16 | 4 | transferable method (industrial anomaly) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Language Modeling; Language Modelling; Prompt Engineering; Prompt Learning | The vision-language model has brought great improvement to few-shot industrial anomaly detection, which usually needs to design of hundreds of prompts through prompt engineering. For automated scenarios, we first use conventional prompt learning with many-class paradigm as the baseline to automatically learn prompts bu... | Papers with Code archive (Jul 2025) | 9.344 |
CKC-A01-067 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 67 | Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection | Chengjie Wang; Wenbing Zhu; Bin-Bin Gao; Zhenye Gan; Jianning Zhang; Zhihao Gu; et al. | 9 | 2024-03-19 | 2,024 | Computer Vision and Pattern Recognition | 2403.12580 | https://arxiv.org/abs/2403.12580 | https://arxiv.org/pdf/2403.12580 | https://huggingface.co/papers/2403.12580 | 10.1109/CVPR52733.2024.02159 | https://www.semanticscholar.org/paper/e42d7cae6a2d94f02796b1d4bfc4d78eb2aaf102 | 243 | 25 | https://github.com/lewandofskee/MambaAD | PwC archive – community implementation | lewandofskee/MambaAD | 220 | 12 | none/unknown | Python | 2025-03-13 | false | 2026-10-16 | 4 | transferable method (industrial anomaly) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Detection; Benchmarking; Unsupervised Anomaly Detection | Industrial anomaly detection (IAD) has garnered significant attention and experienced rapid development. However, the recent development of IAD approach has encountered certain difficulties due to dataset limitations. On the one hand, most of the state-of-the-art methods have achieved saturation (over 99% in AUROC) on ... | Papers with Code archive (Jul 2025) | 9.196 |
CKC-A01-068 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 68 | VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization | Pankaj Mishra; Riccardo Verk; Daniele Fornasier; Claudio Piciarelli; Gian Luca Foresti | 5 | 2021-04-20 | 2,021 | International Symposium on Industrial Electronics | 2104.10036 | https://arxiv.org/abs/2104.10036 | https://arxiv.org/pdf/2104.10036 | https://huggingface.co/papers/2104.10036 | 10.1109/ISIE45552.2021.9576231 | https://www.semanticscholar.org/paper/913d43b69fd4ddee2ff64d3a1e6402d3787e2b7e | 571 | 61 | https://github.com/pankajmishra000/VT-ADL | HF Papers – linked repo | pankajmishra000/VT-ADL | 124 | 25 | MIT | Python | 2025-10-15 | false | 2026-10-16 | 4 | transferable method (industrial anomaly) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | We present a transformer-based image anomaly detection and localization network. Our proposed model is a combination of a reconstruction-based approach and patch embedding. The use of transformer networks helps to preserve the spatial information of the embedded patches, which are later processed by a Gaussian mixture ... | HF Papers | 9.063 | |
CKC-A01-069 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 69 | Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images | Chaoqin Huang; Aofan Jiang; Jinghao Feng; Ya zhang; Xinchao Wang; Yanfeng Wang | 6 | 2024-03-19 | 2,024 | Computer Vision and Pattern Recognition | 2403.12570 | https://arxiv.org/abs/2403.12570 | https://arxiv.org/pdf/2403.12570 | https://huggingface.co/papers/2403.12570 | 10.1109/CVPR52733.2024.01081 | https://www.semanticscholar.org/paper/6c29f50589f8366bbe4f5e94105471b759a8dd24 | 147 | 17 | https://github.com/MediaBrain-SJTU/MVFA-AD | PwC archive – official | MediaBrain-SJTU/MVFA-AD | 249 | 29 | MIT | Python | 2024-04-06 | false | 2026-10-16 | 4 | transferable method (industrial anomaly) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Classification; Anomaly Detection; Anomaly Segmentation | Recent advancements in large-scale visual-language pre-trained models have led to significant progress in zero-/few-shot anomaly detection within natural image domains. However, the substantial domain divergence between natural and medical images limits the effectiveness of these methodologies in medical anomaly detect... | Papers with Code archive (Jul 2025) | 9.058 |
CKC-A01-070 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 70 | MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images | Xurui Li; Ziming Huang; Feng Xue; Yu Zhou | 4 | 2024-01-30 | 2,024 | International Conference on Learning Representations | 2401.16753 | https://arxiv.org/abs/2401.16753 | https://arxiv.org/pdf/2401.16753 | https://huggingface.co/papers/2401.16753 | 10.48550/arXiv.2401.16753 | https://www.semanticscholar.org/paper/991d82387ee1b866a841f4b6bc3cf2a64dd271fc | 108 | 17 | https://github.com/xrli-U/MuSc | PwC archive – official | xrli-U/MuSc | 434 | 36 | MIT | Python | 2026-04-25 | false | 2026-10-16 | 4 | transferable method (industrial anomaly) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anomaly Classification; Anomaly Detection | This paper studies zero-shot anomaly classification (AC) and segmentation (AS) in industrial vision. We reveal that the abundant normal and abnormal cues implicit in unlabeled test images can be exploited for anomaly determination, which is ignored by prior methods. Our key observation is that for the industrial produc... | Papers with Code archive (Jul 2025) | 9.029 |
CKC-A01-071 | A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 71 | Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study | Christoph Baur; Stefan Denner; Benedikt Wiestler; Shadi Albarqouni; Nassir Navab | 5 | 2020-04-07 | 2,020 | Medical Image Anal. | 2004.03271 | https://arxiv.org/abs/2004.03271 | https://arxiv.org/pdf/2004.03271 | https://huggingface.co/papers/2004.03271 | 10.1016/j.media.2020.101952 | https://www.semanticscholar.org/paper/d82800c79dd335297336fe10b1a60d47706e4296 | 402 | 47 | https://github.com/StefanDenn3r/Unsupervised_Anomaly_Detection_Brain_MRI | PwC archive – official | StefanDenn3r/Unsupervised_Anomaly_Detection_Brain_MRI | 185 | 33 | GPL-3.0 | Python | 2023-03-24 | false | 2026-10-16 | 4 | transferable method (industrial anomaly) | Core of the Çekicin damage AI: photo → damage type/severity → tow decision. | false | Anatomy; Anomaly Detection; Anomaly Segmentation; Experimental Design; Medical Image Analysis; Representation Learning; Unsupervised Anomaly Detection; valid | Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these works is to learn a model of normal anatomy by learning to compress and recover healthy data. This allows to spot abnormal structures from erron... | Papers with Code archive (Jul 2025) | 8.912 |
End of preview. Expand in Data Studio
Çekicin AI – Papers with Code database (1,000 papers × 10 areas)
A curated database of 1,000 AI research papers that all have public code (100 per area), selected for the Çekicin roadside-assistance app (tow trucks, damage photos, dispatch, ETA). Every code link was checked live on GitHub on 2026-10-16. Built on the frozen Papers-with-Code archive (Jul 2025) and updated with 2025-26 papers from Hugging Face Papers.
Areas
| ID | Area | Türkçe | Papers | Core (tier 1) | Since Jul 2025 | Median citations | Median ★ |
|---|---|---|---|---|---|---|---|
| A01 | Vehicle damage & defect detection | Araç hasarı ve kusur tespiti | 100 | 7 | 6 | 136 | 101 |
| A02 | Object detection & instance segmentation | Nesne tespiti ve örnek bölütleme | 100 | 49 | 15 | 2,876 | 2,219 |
| A03 | Vision-language models & visual reasoning | Görme-dil modelleri ve görsel akıl yürütme | 100 | 92 | 15 | 675 | 1,398 |
| A04 | Image restoration & quality (low-light, deblur, IQA) | Görüntü onarımı ve kalite (düşük ışık, bulanıklık, IQA) | 100 | 85 | 15 | 670 | 422 |
| A05 | OCR, license plates & vehicle re-identification | OCR, plaka tanıma ve araç yeniden tanıma | 100 | 19 | 15 | 277 | 551 |
| A06 | Traffic accident & video anomaly detection | Trafik kazası ve video anomali tespiti | 100 | 19 | 6 | 166 | 94 |
| A07 | Image forensics & claim-fraud detection | Görüntü adli analizi ve sahte talep tespiti | 100 | 89 | 15 | 158 | 123 |
| A08 | Vehicle routing, dispatch & combinatorial optimization | Araç rotalama, sevk ve kombinatoryal optimizasyon | 100 | 61 | 7 | 105 | 56 |
| A09 | Traffic, ETA & demand forecasting | Trafik, varış süresi (ETA) ve talep tahmini | 100 | 59 | 14 | 388 | 288 |
| A10 | Efficient on-device AI (quantization, pruning, distillation) | Cihaz üstü verimli yapay zekâ (niceleme, budama, damıtma) | 100 | 89 | 15 | 749 | 1,187 |
Totals: 1,000 unique papers (no paper appears in two areas), 2013–2026, 123 papers published after July 2025, 1,874,727 citations, 3,201,154 GitHub stars.
Highlights per area
A01 – Vehicle damage & defect detection. Why: Core of the Çekicin damage AI: photo → damage type/severity → tow decision.
Top: CarDD: A New Dataset for Vision-based Car Damage Detection (code); Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes (code); An Anti-fraud System for Car Insurance Claim Based on Visual Evidence (code)
New (2025-26): CrashSplat: 2D to 3D Vehicle Damage Segmentation in Gaussian Splatting; Small Dents, Big Impact: A Dataset and Deep Learning Approach for Vehi
A02 – Object detection & instance segmentation. Why: Backbone of the on-device YOLO/CarDD models: find the car, parts and damage regions.
Top: Swin Transformer: Hierarchical Vision Transformer using Shifted Window (code); Faster R-CNN: Towards Real-Time Object Detection with Region Proposal (code); Mask R-CNN (code)
New (2025-26): Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models; Real-Time Object Detection Meets DINOv3
A03 – Vision-language models & visual reasoning. Why: Cloud second opinion (Llama 4 / Mistral VLM): describe damage, answer 'is it drivable?'.
Top: Learning Transferable Visual Models From Natural Language Supervision (code); Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at (code); Visual Instruction Tuning (code)
New (2025-26): GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalabl; DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive Wo
A04 – Image restoration & quality (low-light, deblur, IQA). Why: Round-2 tests showed night (recall 0.50) and blur (0.06) break the model — quality gate & enhancement.
Top: The Unreasonable Effectiveness of Deep Features as a Perceptual Metric (code); MUSIQ: Multi-scale Image Quality Transformer (code); Restormer: Efficient Transformer for High-Resolution Image Restoration (code)
New (2025-26): Generative Refocusing: Flexible Defocus Control from a Single Image; Fine-grained Image Quality Assessment for Perceptual Image Restoration
A05 – OCR, license plates & vehicle re-identification. Why: Read plates/registration documents, match the customer's car on arrival, auto-fill job forms.
Top: TransReID: Transformer-based Object Re-Identification (code); FastReID: A Pytorch Toolbox for General Instance Re-identification (code); VehicleNet: Learning Robust Visual Representation for Vehicle Re-ident (code)
New (2025-26): RoundaboutHD: High-Resolution Real-World Urban Environment Benchmark f; dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Lang
A06 – Traffic accident & video anomaly detection. Why: Automatic crash detection from dashcam/phone sensors → proactive tow offer and emergency alert.
Top: Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal R (code); Unsupervised Traffic Accident Detection in First-Person Videos (code); When language and vision meet road safety: leveraging multimodal large (code)
New (2025-26): AccidentBench: Benchmarking Multimodal Understanding and Reasoning in ; Uncovering What, Why and How: A Comprehensive Benchmark for Causation
A07 – Image forensics & claim-fraud detection. Why: Detect edited, re-used or AI-generated damage photos before they reach insurers/operators.
Top: FaceForensics++: Learning to Detect Manipulated Facial Images (code); MesoNet: a Compact Facial Video Forgery Detection Network (code); Multi-attentional Deepfake Detection (code)
New (2025-26): LOTA: Bit-Planes Guided AI-Generated Image Detection; OmniAID: Decoupling Semantic and Artifacts for Universal AI-Generated
A08 – Vehicle routing, dispatch & combinatorial optimization. Why: Assign the nearest suitable tow truck, plan multi-stop routes, balance driver fleets.
Top: Attention, Learn to Solve Routing Problems! (code); POMO: Policy Optimization with Multiple Optima for Reinforcement Learn (code); Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWA (code)
New (2025-26): G-LNS: Generative Large Neighborhood Search for LLM-Based Automatic He; One Step is Enough: Multi-Agent Reinforcement Learning based on One-St
A09 – Traffic, ETA & demand forecasting. Why: Accurate tow ETA, predicting breakdown demand by hour/region to pre-position trucks.
Top: Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framewor (code); T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction (code); Connecting the Dots: Multivariate Time Series Forecasting with Graph N (code)
New (2025-26): Externalizing Research Synthesis and Validation in AI Scientists throu; How Different from the Past? Spatio-Temporal Time Series Forecasting w
A10 – Efficient on-device AI (quantization, pruning, distillation). Why: Run damage models offline on cheap Android phones; cut cloud cost (Workers AI neuron limit).
Top: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision (code); MobileNetV2: Inverted Residuals and Linear Bottlenecks (code); SegFormer: Simple and Efficient Design for Semantic Segmentation with (code)
New (2025-26): QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Langu; DreamLite: A Lightweight On-Device Unified Model for Image Generation
Columns (config papers)
id (e.g. CKC-A01-001) · area_id · area · area_tr · rank_in_area · title · authors · date · year · venue ·
arxiv_id · arxiv_url · pdf_url · hf_paper_url · doi · semantic_scholar_url · citations · influential_citations ·
code_url · code_source · github_repo · github_stars · github_forks · github_license · github_language · github_last_push ·
github_archived · code_verified_on · relevance_tier (1 core → 4 transferable) · relevance · why_for_cekicin · is_2025_26_update ·
tasks · abstract · source · score
from datasets import load_dataset
papers = load_dataset("sosa123454321/cekicin-ai-papers", "papers", split="train")
new_damage = papers.filter(lambda r: r["area_id"] == "A01" and r["is_2025_26_update"])
How papers were selected
- Pool: 576,261 papers in the Papers-with-Code archive → 169,291 with code; plus Hugging Face Papers search (5 queries per area, repos only) and a targeted arXiv search for vehicle-damage papers (rare in other indexes).
- Relevance: each area has tiers (1 = Çekicin-specific, e.g. car damage, license plate, vehicle routing; 2–3 = related/foundation). A paper must match by task tag or title/abstract keywords and pass a topic check (keyword in title or ≥2× in abstract).
- Ranking (within each area: relevance tier first, then score):
log(1+citations) + 0.5·log(1+GitHub stars) + 0.3·official-code + tier bonus (3 / 1.5) + recency bonus (2024 +1, 2025+ +2). Citations from Semantic Scholar, stars from GitHub (Oct 2026). - Balance: per area ≥15 papers from Jul 2025 onwards where available and ≥35 from 2023 onwards; the rest by score. Each paper is assigned to one area only (most specific wins).
- Code check: all repos queried via the GitHub API; 13 dead/renamed repos were dropped and replaced.
Caveats
code_sourcetells you how the link was found. "PwC archive – community implementation" (126) is a third-party re-implementation; 6 rows from arXiv search were matched by README/HF and should be double-checked.- Vehicle-damage papers with code are scarce: area A01 has only 7 car/vehicle-damage-specific papers and is filled with road/building damage, crack/defect and industrial-anomaly methods that transfer to the task. Areas A01, A06 and A08 have fewer than 15 post-July-2025 papers because fewer recent papers with code exist in the indexes used.
- Citation counts favour older papers; use
is_2025_26_updateor sort bydateto see the newest work. - Code licences vary (
github_license); 294 repos declare no licence — do not reuse their code commercially without permission. Ultralytics YOLO repos are AGPL-3.0.
Sources & licence
Metadata from the Papers-with-Code archive (CC-BY-SA-4.0), arXiv, Hugging Face Papers,
Semantic Scholar and GitHub. This dataset is released under CC-BY-SA-4.0. Rebuild scripts in scripts/ (set GITHUB_TOKEN).
Related: cekicin-ai-knowledge · cekicin-damage-benchmark · AI Lab
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