Image Detection of Metal Surface Defects Based on Improved YOLOX-S Network

Abstract

In order to realize the image detection and identification of surface defects in the manufacturing and processing of metal parts, improve the image detection accuracy of unqualified products in the process of metal parts processing assembly line, improve the automation level of equipment in the process of metal defect detection, and solve the problems of easy fatigue, low detection effi-ciency, low detection accuracy, strong subjectivity and inability to adapt to the detection of large quantities of high-quality parts in manual detection. A convolution neural network model based on improved YOLOX-S is proposed for metal surface defect image detection. The model changed the structure of the unit module and introduced the attention mechanism module based on the original YOLOX-S model, which optimized the model parameters and improved bounding box position regression loss function and confidence loss function, and then the image detection model of common defects on the metal surface was constructed and realized. The results showed that the loss of improved YOLOX-S model could converge faster, the model with bounding box position regression loss function and attention mechanism added, its mAP increased from 94.23% to 96.14%, and the accuracy improvement effect is the best, while only a small amount of reasoning time is increased. Compared with the YOLOX-S model and the model with different improved methods, it is shown that the improved model based on bounding box position regression loss function and attention mechanism has the best comprehensive recognition effect, which can meet the require-ments of metal surface defect image detection and reduce the outflow of defective products in the production process of metal parts.