Computer Vision-Based Vehicle Damage Assessment for Intelligent Auto Insurance Claim Processing

Abstract

The increasing number of road accidents has led to a growing demand for efficient and reliable vehicle damage assessment systems within the auto insurance industry. Traditional claim inspection methods rely heavily on manual evaluation performed by human surveyors, which is often time-consuming, subjective, and prone to inconsistencies. Recent developments in computer vision and deep learning provide an opportunity to automate this process and improve the overall efficiency of insurance claim management. This research presents a computer vision–based framework for automated vehicle damage assessment designed to support intelligent auto insurance claim processing. The proposed system analyzes accident images captured by drivers or inspection systems and identifies damaged vehicle components through deep learning–based object detection techniques. The framework incorporates image preprocessing, damage detection, damage classification, and decision-support mechanisms to generate structured information that can assist insurance providers in evaluating claims. Experimental evaluation demonstrates that the proposed approach can effectively detect multiple categories of vehicle damage and significantly reduce the time required for claim assessment. The results highlight the potential of artificial intelligence–driven inspection systems to improve accuracy, accelerate claim processing, and enhance customer experience in modern insurance services.