Car Damage

Automated car damage detection is a rapidly developing field driven by the needs of the insurance and automotive repair industries for faster, more accurate damage assessment. Current research focuses on improving the accuracy of instance segmentation and object detection using deep learning models, such as Mask R-CNN, Yolact, and SipMask, often incorporating self-supervised learning and attention mechanisms to refine damage identification and localization in images. The development of large, publicly available datasets like CarDD is crucial for training and evaluating these models, ultimately leading to more efficient and reliable automated damage assessment processes.