Surface Defect Detection of Copper Tube Based on YOLOX with Convolutional Block Attention and Adaptive Spatial Feature Fusion

Surface defect detection technology is important to improve product quality and save production costs. In order to realize automatic detection of copper tube surface defects, an improved YOLOX algorithm is proposed based on convolutional block attention (CBA) and adaptive spatial feature fusion (ASFF), named as CBA-ASFF-YOLOX. The improved YOLOX backbone feature extraction network is replaced with CSPDarknet-53. Then, we construct a convolutional block attention module and an adaptively spatial feature fusion module in the feature fusion part to enhance the spatial position correlation between features by learning the connections between different feature maps. To solve the unbalanced differences in real label samples, we utilize Focal Loss function to replace the cross-entropy loss function. In addition, the bounding box regression loss in this study is based on the SIoU formulation, and a simplified variant is adopted to improve regression quality and facilitate more stable convergence. Finally, the algorithm is applied to copper tube surface defect detection task. Experimental results show that the accuracy of CBA-ASFF-YOLOX algorithm is higher than those of other series of YOLO algorithm.