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

Xinkuang Wang^1,2 Wenjing Li^1,2 Zhongcheng Wu^1,2

1. Chinese Academy of Sciences, China
2. University of Science and Technology of China, China

wangxk0624@mail.ustc.edu.cn

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 high-resolution car damage images with over 9,000 wellannotated instances of six damage categories (examples are shown in Figure 1). 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 Overview

Materials

Paper

Dataset

Codes

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Citation

@article{CarDD,
    author={Wang, Xinkuang and Li, Wenjing and Wu, Zhongcheng},
    journal={IEEE Transactions on Intelligent Transportation Systems}, 
    title={CarDD: A New Dataset for Vision-Based Car Damage Detection}, 
    year={2023},
    volume={24},
    number={7},
    pages={7202-7214},
    doi={10.1109/TITS.2023.3258480}
}