Research on Damage Detection and Recognition System for Automotive Components Based on Stereo Vision and Deep Learning

Abstract

Damage detection in automotive components is of paramount importance for ensuring vehicle safety and performance. However, traditional detection methods suffer from significant limitations in both efficiency and accuracy. The recent advancements in deep learning and stereo vision technologies have introduced innovative approaches for intelligent damage detection in complex scenarios. This study proposes a damage detection and recognition system for automotive components that integrates stereo vision and the YOLOv5 deep learning algorithm. The research methodology includes constructing a stereo vision data acquisition platform, conducting image preprocessing and depth information extraction, applying various data augmentation techniques to enhance sample diversity, and leveraging transfer learning and hyperparameter optimization to improve model performance. Experimental results demonstrate that the system exhibits excellent performance in detection accuracy, real-time capability, and adaptability to small objects, effectively identifying diverse types of component damage. This research provides a reliable technological foundation for intelligent detection tasks in complex industrial scenarios, contributing significantly to improving quality control efficiency.