PCB-YOLOV8X: a network for detecting micro-sized defects on PCB surfaces based on enhanced feature information

To address the challenges in PCB surface defect detection, including high labor costs, low detection efficiency, and insufficient detection accuracy, this study innovatively proposes a PCB surface micro-defect detection network called PCB-YOLOV8X based on the YOLOv8 model. By integrating the C2f-DCNV2 adaptive feature extraction module and the SPPF-LSKA efficient feature enhancement module, the network significantly improves the detection accuracy and efficiency for micro-sized defects. Furthermore, an optimized IWD-CIoU loss function is proposed to more precisely evaluate the matching degree between predicted and ground truth bounding boxes. In comparative experiments with different models, PCB-YOLOV8X achieves optimal performance on key metrics, including mAP@0.5 of 98.44%, Precision of 97.67%, and Recall of 96.34%. Compared with the traditional YOLOv8 model, these represent improvements of 2.76% in mAP@0.5, 2.33% in Precision, and 2.59% in Recall. The proposed PCB-YOLOv8X demonstrates enhanced capability in feature extraction and fusion in PCB surface micro-defect detection tasks, effectively solving issues such as low accuracy, missed detections, and false alarms in existing PCB defect detection models.