The existing defect detection methods for PCB bare boards have problems such as low accuracy, poor real-time performance, and difficulty in deploying on mobile terminals. Based on the YOLOv4 algorithm as the basic framework to improve it, this paper proposes a defect detection algorithm specifically for PCB bare boards. In response to the problem that the YOLOv4 algorithm is difficult to deploy on the mobile terminal, the proposed improved algorithm uses GhostNet instead of CSPDarknet53 to lighten the entire detection network. In order to make up for the lack of performance of YOLOv4 algorithm in multi-scale feature fusion, this paper proposes a bidirectional adaptive feature fusion network AF-BiFPN to replace the PANet network in YOLOv4 algorithm. In order to further improve the detection accuracy of the model, the m-ECANet channel attention mechanism is inserted in the sampling process of the AF-BiFPN feature fusion network. The experimental results show that the model size of the improved YOLOv4 algorithm is 18. 64 MB, the mean average precision (mAP) of detection is 98. 39%, and the detection speed is 62. 23 FPS, which can provide theoretical guidance for actual PCB bare board detection.