• cloud-based deployment
• damage localization
• deep learning
• instance segmentation
• insurance automation
• multi-instance segmentation
• real-time inference
• scalable AI systems
• vehicle damage assessment

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% mAP@0.5 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.