A YOLOv8-PCB (printed circuit boards) defect detection model has been proposed to address the challenge of identifying small irregularly shaped surface defects on PCBs. This model incorporates the WIoUv3 loss function, which reduces penalties for low-quality anchor boxes, thereby accelerating algorithm convergence. It integrates shallow scale and small object detection heads to capture small defect features. The ADown downsampling technique is used in the backbone network to prevent excessive loss of contextual information while reducing the feature map's size. Furthermore, combining dynamic upsampling in the feature pyramid further improves the feature map's resolution, enhancing the model ability to detect PCB defect details. Experimental results show that the proposed model achieves an average accuracy of 98.37% and a recall rate of 96.39%. Compared with the benchmark model, average accuracy has increased by 3.62 percentage points, and the recall rate has risen by 5.49 percentage points. These enhancements significantly reduce missed detections and boost the model's overall detection performance.