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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月27日

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  • Xiaoyan Xu,
  • Jennifer C. Dela Cruz,
  • Ye Wang

摘要

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.