A Lightweight and High-Precision PCB Surface Defect Detection Method Based on YOLOv8

In response to the diverse types and large number of PCB surface defects, our paper proposes an improved YOLOv8-based method for PCB surface defect detection. First, a lightweight modification is performed by introducing RepGhostBottleNeck as the lightweight backbone network, which reduces the number of parameters in the training model. It should be noted that the term “lightweight” in this paper is relative to the original YOLOv8L baseline. Compared with extremely lightweight detectors, the model in this paper places greater emphasis on the balance between accuracy and efficiency. Additionally, an attention mechanism module and a small object detection head module are added to the backbone network. Furthermore, the loss function of the network is improved. Experimental results show that the improved model achieves an average mAP@0.5 of 0.976, demonstrating high-precision detection on the constructed dataset.