Abstract: The existing PCB defect detection methods have low accuracy and high model complexity. To solve this problem, a Deep PCB defect detection algorithm based on improved YOLOv5s is proposed. The algorithm adds CBAM attention mechanism after C3 layer of the Neck network, establishes feature mapping relationship for small target detection, reconstructs attention on feature map, endows small targets with higher feature weight, and improves the network’s feature extraction ability for small targets in PCB. In order to fundamentally solve the problem of target feature redundancy, realize the lightweight of the network, and ensure the accuracy of network detection, it is proposed to replace the Conv module with the Ghost Conv module, and change the C3 module to the Ghost Bottleneck module. The EIOU Loss function is used to replace the CIOU Loss function, reducing the real difference between the width and height of the prediction frame and the confidence level, and reducing the regression loss of the network. Experiments are carried out using the Deep PCB dataset published by the Image Processing and Pattern Recognition Research Institute of Shanghai Jiao Tong University. The results show that compared with YOLOv5s, when IOU=0.5, the mAP of this algorithm is increased by 6.8%, the speed is increased by 4.7 Fps, the model size is decreased by 2.9 M, and the calculation amount is reduced by 2.8 G.