CarDD: A New Dataset for Vision-based Car Damage Detection
This addresses a data bottleneck for car insurance businesses by providing a foundational resource, though it is incremental as it focuses on dataset creation rather than novel algorithmic advances.
The authors tackled the lack of public datasets for car damage detection by introducing CarDD, a large-scale dataset with 4,000 high-resolution images and over 9,000 annotated instances across six damage categories, and they conducted experiments with state-of-the-art deep learning methods to analyze its specialty.
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.