Mechanical Surface Defect Detection Based on Deep Learning
This paper proposes a deep learning based mechanical surface defect detection method and conducts empirical verification on the MVTec AD dataset.Firstly,a machine vision based mechanical surface detection system was designed to provide high-quality input data for deep learning models.Secondly,in-depth research was conducted on image enhancement techniques,which improved image quality through operations such as brightness adjustment,contrast enhancement,and histogram equalization to more accurately capture the subtle features of surface defects.In terms of defect detection methods,the YOLOv5 model was adopted.Finally,a series of experiments were conducted using the MVTec AD dataset to validate the effectiveness and generalization ability of the proposed method in real-world industrial scenarios.The results show that the model exhibits good misdetection avoidance ability on normal images and has high recognition accuracy for real defect areas.
deep learningimage enhancementYOLOv5 modelsurface detection
NETL
NSTL
万方数据
