AI-Based Vehicle Damage Detection, Cost Estimation, and Insurance Claim Prediction System

Abstract

Road accidents result in billions of rupees worth of vehicle damage every year, yet the process of assessing that damage and processing insurance claims has remained stubbornly manual, slow, and error-prone. This paper presents the design, implementation, and evaluation of an AI-based vehicle damage detection and cost estimation system that automates the entire assessment pipeline through the integration of deep learning and machine learning. The proposed system is a Flask-based web application in which a pre-trained YOLOv8 object detection model identifies damaged vehicle components from user-uploaded photographs, including parts such as bonnets, bumpers, doors, and fenders. Detected parts are cross-referenced against a structured JSON pricing database to compute a repair cost estimate using a part-wise summation formula. A rule-based module predicts probable internal damages from observed external patterns, and a trained scikit-learn classification model determines insurance eligibility and computes coverage. The system classifies overall damage severity into minor, moderate, and major categories and produces smart repair and financial recommendations. Experimental results demonstrate that the YOLOv8 model achieves a mean Average Precision (mAP@50) of 0.79 across seven damage classes, with a cost estimation mean absolute percentage error of 11.4% relative to authorised workshop quotations. The integrated pipeline reduces a conventionally multi-day assessment process to under ten seconds, offering a practical decision-support tool for vehicle owners, insurance companies, and repair workshops.