International Journal of Scientific Research and Engineering Development

Vehicle damage assessment is a critical requirement in automobile maintenance, insurance claim verification, and fleet management operations. Conventional inspection methods rely on manual evaluation, which is time-consuming, inconsistent, and highly susceptible to human error. This paper proposes an intelligent framework that automates vehicle damage detection and severity estimation using Deep Learning and Computer Vision techniques. The system employs YOLOv10 as the core detection architecture to accurately identify and localize damaged regions within vehicle images. Four primary damage categories are recognized by the framework, namely scratches, dents, cracks, and broken vehicle components. Detected damage instances are further classified into three severity levels — minor, moderate, and severe — to support informed repair and claim decisions. Prior to detection, images undergo a structured preprocessing pipeline involving resizing, normalization, and noise filtering to ensure consistent input quality. The framework additionally supports automated report generation, enabling seamless integration into vehicle service platforms and insurance processing workflows. Experimental evaluation demonstrates that the proposed system achieves a mean average precision of 88.7% with an inference time of 15 milliseconds per image. The results confirm that the proposed approach offers a scalable, reliable, and efficient solution for AI-driven automated vehicle damage analysis.

M. Vasuki, M. Karthika,"AI-Based Vehicle Damage and Safety Analysis" International Journal of Scientific Research and Engineering Development, V9(3): Page(1939-1944) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.