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

• URL: http://arxiv.org/abs/2211.00945v2
• Date: Mon, 28 Aug 2023 11:36:06 GMT
• Title: CarDD: A New Dataset for Vision-based Car Damage Detection
• Authors: Xinkuang Wang, Wenjing Li, Zhongcheng Wu
• Abstract summary: 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.
• Score: 13.284578516117804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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