Method of PCB defect detection with yolov5 algorithm by adding transformer module

For the current problems of low detection accuracy and slow detection speed of PCB board defect detection, this paper proposes a method of PCB defect detection by YOLOv5 algorithm with Transformer module added. The algorithm is using Transformer encoder block to replace some convolution blocks and bottleneck blocks in YOLOv5. it uses the self-attention mechanism to tap the feature representation potential and solve the problem of low resolution of the feature map at the end of the network. The experimental results show that the improved algorithm can better identify the defects of PCB boards, the detection accuracy mAP reaches 97.8%, and the average detection time is improved from 194.2ms to 183.5ms. it is suitable for the actual production and inspection process.