Research and Design of an Electric Vehicle Damage Recognition and Evaluation System Based on Machine Vision

Abstract

The rapid expansion of the electric vehicle (EV) industry has made damage recognition and evaluation a critical issue. Traditional manual damage assessment methods are inefficient and susceptible to subjective bias, making it challenging to meet the increasing demand for precise and efficient damage evaluation in the EV repair and insurance sectors. To address this challenge, this study proposes a machine vision-based damage recognition and evaluation system for electric vehicles. The system integrates image processing, deep learning algorithms, and attention mechanisms to autonomously identify damage types and quantify their severity. By utilizing high-resolution image acquisition, deep learning feature extraction, damage classification, and regression analysis, the system not only enhances the efficiency of damage recognition but also improves the accuracy of damage assessment. Performance evaluation results indicate that the system performs stably and adaptively across various environments and vehicle types, effectively handling complex damage scenarios in electric vehicles. The novelty of this research lies in its application of machine vision and deep learning techniques to automate the damage evaluation process, filling a gap in the field and providing a smart and efficient solution for the EV industry.