Abstract: The "Intelligent Vehicle Damage Assessment & Cost Estimator" project aims to assist vehicle owners by providing accurate and automated damage assessment using advanced deep learning techniques. Leveraging YOLOv5, a state-of- the-art object detection model, this system is designed to analyze and evaluate damage sustained by vehicles, including four-wheelers. The model is trained on a comprehensive dataset of vehicle damages to accurately identify and classify different types of damage. By integrating this technology, users can independently assess vehicle damage, reduce dependency on manual inspection, minimize errors, and obtain precise cost estimations for repairs, ensuring transparency and better decision making.

Keywords: Vehicle damage assessment, cost estimation, YOLOv5, user assistance, deep learning, object detection, two- wheeler, four-wheeler.

Downloads: | DOI: 10.17148/IARJSET.2026.13450