Printed circuit boards are the core and most basic components of many electronic products, and their defect detection has the characteristics of high complexity and small defect targets. An improved YOLOv4-tiny printed circuit board defect detection method is proposed to meet the detection speed. On the premise of improving the detection accuracy. First, a spatial pyramid pooling module is added to the backbone network to reduce network parameters and improve network prediction speed while using the local and global features of the image to fuse multiple receptive fields; secondly, add a convolutional block attention module in the FPN part to further Enhance the effect of feature fusion at different stages and improve the accuracy of target detection for small target defects. Finally, Adam optimizer is used to improve the convergence speed and accuracy of the regression process, and the cosine annealing decay and label smoothing strategies are used to optimize the network loss function. In order to suppress the overfitting problem during network training. By using the improved algorithm to conduct comparative experiments on the printed circuit board defect data set, it shows that the weight file of the algorithm in this paper is only 22.85M, the average detection accuracy is improved by 13.38% compared with the original algorithm, and the detection speed reaches 149.03FPS (on GeForce RTX3060). ), with better effectiveness and feasibility.