CarDD: A New Dataset for Vision-based Car Damage Detection
Abstract
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-scale dataset designed for vision-based car damage detection and segmentation. Our CarDD contains 4,000 highresolution car damage images with over 9,000 well-annotated instances of six damage categories. We detail the image collection, selection, and annotation processes, and present a statistical dataset analysis. Furthermore, we conduct extensive experiments on CarDD with state-of-the-art deep methods for different tasks and provide comprehensive analyses to highlight the specialty of car damage detection. CarDD dataset and the source code are available at https://cardd-ustc.github.io.
- Publication:
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arXiv e-prints
- Pub Date:
- November 2022
- DOI:
- 10.48550/arXiv.2211.00945
- arXiv:
- arXiv:2211.00945
- Bibcode:
- 2022arXiv221100945W
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition
- E-Print:
- 13 pages, 10 figures, full-length paper for Transactions on Intelligent Transportation Systems (2023)