CropResMoE-50: A Region-Aware Mixture-of-Experts Framework for Fine-Grained Vehicle Damage Detection and Semi-Automated Annotation

Accurate detection of vehicle damages such as dents, scratches, and cracks is essential for improving the efficiency, consistency, and scalability of insurance claim assessment. Conventional inspection procedures rely heavily on manual evaluation, making them time-consuming, subjective, and costly. To address these limitations, this paper presents a three-stage progression of mixture-of-experts (MoE)–based classification models trained on the CarDD dataset, which contains 4,000 COCO-annotated ve