Aimed at the defect of printed circuit board (PCB) in the process of actual production style variety and small defects,difficult located defect position, and a huge model is difficult to achieve the requirements of real-time detection,and a large number of the depth of the separable convolution layer established lightweight model can't achieve enough accuracy, this paper proposes a PCB defect detection algorithm based on YOLOv5s. Therefore,the original Backbone Conv module and C3 module are replaced by GhostConv.In the Neck part,a new lightweight convolution technology GSConv is introduced to reduce the size of the model while maintaining the accuracy.GSConv achieves an excellent trade-off between the accuracy and speed of the model.Aiming at the problem that many attention modules cannot pay attention to global information while the model is large,a multi-scale lightweight double channel depthwise attention module (DWAM) is proposed to further improve the model accuracy.The experimental results show that, the average mAP of all categories of the improved algorithm is 99.14%,and the GFLOPS of the model is 7.194 G,and the Params is 7.175.The average mAP of the original YOLOv5s is 96.86%,and the GFLOPs is 6.89 G,and the Params is 6.596.Although Params and GFLOPs have increased,they still meet the requirements of lightweight network,and the accuracy is improved by 2.25% compared with YOLOv5s,and the defect recognition accuracy of each category has been improved,which greatly reduces the computation amount and model parameters while ensuring the accuracy.It can meet the demand of industrial testing and production and facilitate mobile terminal deployment.