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.
@article{arxiv.2211.00945,
title = {CarDD: A New Dataset for Vision-based Car Damage Detection},
author = {Xinkuang Wang and Wenjing Li and Zhongcheng Wu},
journal= {arXiv preprint arXiv:2211.00945},
year = {2023}
}
Comments
13 pages, 10 figures, full-length paper for Transactions on Intelligent Transportation Systems (2023)