论文标题
卡德：一个新的数据集，用于基于视觉的汽车损伤检测
CarDD: A New Dataset for Vision-based Car Damage Detection
论文作者
论文摘要
自动汽车损坏检测引起了汽车保险业业务的极大关注。但是，由于缺乏高质量和公开可用的数据集，我们几乎无法学习可行的汽车损伤检测模型。为此，我们贡献了汽车伤害检测（CARDD），这是第一个专为基于视觉的汽车损坏检测和分割而设计的公共大型数据集。我们的卡德包含4,000个高分辨率的汽车伤害图像，其中有9,000多个六个损害类别的井井有条。我们详细介绍图像收集，选择和注释过程，并提供统计数据集分析。此外，我们对CARDD进行了广泛的实验，采用最先进的深度方法来进行不同的任务，并提供全面的分析，以突出汽车损伤检测的专业。 CARDD数据集和源代码可在https://cardd-ustc.github.io中找到。
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.
