The bare complex layout of PCBs cause low contrast, uneven brightness, small defect positions, and irregular shapes in detected images, resulting in a large number of parameters, overfitting, and loss of feature information with increasing network depth. In this study, a PCB detection model PA-YOLO v5 based on YOLO v5 and mixed attention mechanism fusion with higher accuracy is proposed to suppress interference from general features and ensure that the network pays more attention to the detailed features of defect targets during feature extraction. The adaptive bidirectional feature pyramid network(BiFPN) is taken as reference to fully utilize the different scales of each feature map, thereby assigning different weights to different detection targets, to improve the network's ability to express various features. Finally, the FReLU activation function is used to expand the ReLU space into a 2D activation function, which enhances the receptive field's ability to capture details and improves model robustness and generalization. Six types of defects were tested using the DeepPCB dataset, and the experimental results showed that the proposed PA-YOLO v5 detection model achieved an accuracy of 99.4%. The effectiveness of the model was verified through ablation and comparative experiments.