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.

翻译：自动汽车损伤检测在汽车保险行业中引起了广泛关注。然而，由于缺乏高质量且公开可用的数据集，我们难以学习到可行的汽车损伤检测模型。为此，本研究贡献了Car Damage Detection（CarDD），这是首个面向基于视觉的汽车损伤检测与分割的大型公开数据集。我们的CarDD包含4,000张高分辨率汽车损伤图像，涵盖6类损伤类别、超过9,000个标注精细的实例。我们详细阐述了图像采集、筛选及标注流程，并进行了统计性的数据集分析。此外，我们采用当前最先进的深度方法在CarDD上开展了多项任务的广泛实验，通过全面的分析凸显了汽车损伤检测的特殊性。CarDD数据集与源代码可通过https://cardd-ustc.github.io获取。