Vehicle Damage Identification using Deep Learning Techniques

In this innovative study, a cutting-edge approach harnessing YOLOv8 and ResNet-50 is proposed for an advanced vehicle damage detection system. Integrating damage segmentation and a meticulous repairing management system, this solution aims to revolutionize workshop operations. Through the utilization of sophisticated algorithms, the system accurately identifies and categorizes vehicular damages, facilitating precise and efficient repair strategies. Moreover, this comprehensive system incorporates an intelligent airbag recommendation feature, optimizing safety measures based on the detected damages. The integration of an accident dashboard further enhances the system's functionality, providing real-time insights into accident patterns and trends. This holistic approach not only streamlines workshop workflows but also prioritizes safety and precision in vehicular repair processes, marking a significant advancement in vehicle damage detection technology.