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arXiv · 2211.00945

CarDD: A New Dataset for Vision-based Car Damage Detection

Xinkuang Wang·Wenjing Li·Zhongcheng Wu
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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.

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Xinkuang Wang, Wenjing Li, Zhongcheng Wu. 2022-11-02. CarDD: A New Dataset for Vision-based Car Damage Detection. https://doi.org/10.1109/tits.2023.3258480

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