Development of Deep Learning Systems for Vehicle Damage Detection and Segmentation

Automated vehicle damage detection is increasingly critical for enhancing the efficiency of insurance claims and reducing manual inspection efforts. This research presents a YOLOv11-based instance segmentation system tailored to identify and segment various damage types as dents, scratches, cracks, tireflat, lamp broken and glass shatter under real-world conditions including occlusions and varying lighting. To ensure robustness, the model is trained on a diverse, multi-source dataset combining CarDD and internal inspection data from Wondersys Srl. The proposed system achieves real-time, cloud-based inference using Google Vertex AI, enabling scalable deployment. Experimental evaluations reveal that YOLOv11 outperforms models such as Mask R-CNN, U-Net, DeepLabV3, and prior YOLO versions (v8, v9) in both segmentation accuracy and inference speed. The model attains 85% [email protected] and high IoU scores across multiple damage classes, confirming its suitability for practical, high-throughput applications. This work delivers a cost-effective and scalable AI solution for automated damage assessment, offering significant improvements for modern insurance and fleet management systems.