Abstract: Aiming at  the problem of unclear  small-target features and insufficient detection accuracy in the defect detection task of industrial printed circuit boards, we proposed an improved  algorithm based on YOLOv8 algorithm. Firstly, we  adapted  the defect detection of printed circuit boards by adding or deleting the feature map sizes, and drew on the experience of  the weighted bi-directional feature pyramid network structure to retain the features of the original image. Secondly, we designed a lightweight module at the neck  for feature extraction by using group convolution, which  improved the detection accuracy while reducing the complexity of the model. Finally, before the  small-object detection head, we introduced coordinate attention module that could  enhance feature representation capabilities, further improving the inspection accuracy. The experimental results show that the improved algorithm can improve the detection accuracy  mAP@0.5 to  95.4% and achieve a detection speed FPS (frames per second) of 105.4, which  can better  meet the  requirements of industrial detection for accuracy and real-time performance.

Key words: defect detection of , printed circuit board, neural network, attention mechanism, group convolution