  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.
