The PPLCFaster-YOLOv5 model is proposed to address the problems of low accuracy, low recall and poor real-time performance of existing PCB surface defect detection methods. The method uses the modified PPLC-Net as the backbone network and the Focus structure as layer 0 of the network to improve the feature map′s ability to express location information. A channel blending mechanism is introduced within the depth-separable convolutional structure so that the features obtained by each grouped convolution have equal contribution to the global features; a Dropout mechanism is incorporated to limit the imbalance regularisation factor. A low parametric number G4Head feature fusion network structure is proposed to incorporate more shallow information into the feature fusion to improve the model′s ability to locate defects; add residual connections between the backbone network and feature fusion to improve the contribution of backbone network information to feature fusion; and adopt the SIOU loss function to accelerate the convergence of the regression frame. The trained model was deployed using the Flask server framework. Experiments show that the deployed PPLCFaster-YOLOv5 model can detect surface defects on DeepPCB as well as the Peking University PCB surface defect detection dataset in 0.009 s, and the accuracy and recall rates are improved compared with other mainstream models.