A System for Automated Vehicle Damage Localization and Severity Estimation Using Deep Learning

ABSTRACT

This paper introduces a sophisticated and comprehensive system for automated vehicle damage detection and severity estimation by leveraging the advanced YoloV8 deep learning model. The proposed solution operates through a web-based interface developed using Python, HTML, CSS, and JavaScript, hosted on the Flask framework. It supports three operational modes: image-based, video-based, and webcam-based predictions, thereby enabling real-time as well as post-incident evaluations. The model is trained on a custom-labeled dataset of 778 images that cover eight distinct vehicle damage categories. Achieving an accuracy rate of 91%, the system enhances reliability in assessing vehicle damages. Designed for scalability and versatility, this system offers valuable applications in domains such as automotive insurance claims, vehicle repair cost assessments, accident investigation, and fleet management.

Keywords:

Vehicle damage detection, YoloV8, deep learning, severity estimation, automated assessment, image classification, real-time analysis, web-based interface, Flask framework.