Metal Surface Defect Detection Method Based on TCM-YOLO Network

Abstract

Surface defect detection in metal production and manufacturing suffers from problems of low detection accuracy and slow processing speed. To address these problems, this study proposes a metal defect detection method based on an improved You Only Look Once version 8 (YOLOv8) network (TCM-YOLO). This method enhances the coordinate attention mechanism to the Three-Channel Coordinate Attention (TCCA) mechanism and combines it with a second version of the deformable convolutional network, i.e., the Three-channel Deformable Convolution Network (TDCN), thereby enhancing the feature extraction ability of the network. In the feature fusion network, a bidirectional feature pyramid and Dynamic Snake Convolution (DSC) are combined to improve the missed detection rate in steel strip defect detection, and to improve the retention of tiny texture and complex defect structure information. The Minimum Point Distance Intersection over Union (MPDIoU) loss function is used to replace the original loss function to accelerate the convergence speed and improve regression accuracy. Finally, a global attention mechanism is embedded to continuously capture important information regarding the global shape of the defect. Experimental results show that the average accuracy of the TCM-YOLO algorithm on the steel strip defects dataset of Northeastern University is 81.8%, which is 7.4 percentage points higher than that of the original YOLOv8 algorithm, and the accuracy reaches 78.3%, which is 8.9 percentage points higher than that of the original model. The detection speed of the algorithm reaches 61.73 frame/s. On the Tianchi aluminum profile defect dataset, the average accuracy is 4.1 percentage points higher than that of the original YOLOv8 algorithm and 8.7 percentage points higher than that of the original model. The results show that the TCM-YOLO algorithm has high detection accuracy and fast detection speed, which improves the detection capability for metal surfaces.