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<p>Detecting micro-defects in densely populated printed circuit boards (PCBs) with complex backgrounds is a critical challenge. To address the problem, the DHNet, a small object detection network based on YOLOv8 employing multi-scale convolutional kernels is proposed for feature extraction and fusion. The lightweight VOVGSHet module is designed for feature fusion and a pyramid structure to efficiently leverage feature map relationships while minimizing model complexity and parameters. Otherwise, to optimize the original extraction structure and enhance multi-scale defect detection, convolutional kernels of varying sizes process the same input channels. Additionally, the incorporation of the Wise-IoU loss function improves small defect detection accuracy and efficiency. Moreover, extensive experiments on a custom PCB dataset demonstrate DHNet's effectiveness, achieving an outstanding mean Average Precision (mAP) of 84.5%, surpassing the original YOLOv8 network by 4.0%, with parameters only of 2.85&#xa0;M. Model demonstrates a latency of 3.6&#xa0;ms on NVIDIA 4090. However, YOLOv8n has a latency of 4.4&#xa0;ms. Validation on public DeepPCB and NEU datasets further confirms DHNet's superiority, which can reach 99.1% and 79.9% mAP, respectively. Finally, successful deployment on the NVIDIA Jetson Nano platform validates DHNet's suitability for real-time defect detection in industrial applications.</p>

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DHNet: a surface defect detection model utilizing multi-scale convolutional kernels

  • 期刊论文

  • 发表日期：

    2025年1月17日

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  • Yingying Zhang,
  • Shuo Wang,
  • Jinhai Wang,
  • Yu Zhao,
  • Zhiwei Chen

摘要

Detecting micro-defects in densely populated printed circuit boards (PCBs) with complex backgrounds is a critical challenge. To address the problem, the DHNet, a small object detection network based on YOLOv8 employing multi-scale convolutional kernels is proposed for feature extraction and fusion. The lightweight VOVGSHet module is designed for feature fusion and a pyramid structure to efficiently leverage feature map relationships while minimizing model complexity and parameters. Otherwise, to optimize the original extraction structure and enhance multi-scale defect detection, convolutional kernels of varying sizes process the same input channels. Additionally, the incorporation of the Wise-IoU loss function improves small defect detection accuracy and efficiency. Moreover, extensive experiments on a custom PCB dataset demonstrate DHNet's effectiveness, achieving an outstanding mean Average Precision (mAP) of 84.5%, surpassing the original YOLOv8 network by 4.0%, with parameters only of 2.85 M. Model demonstrates a latency of 3.6 ms on NVIDIA 4090. However, YOLOv8n has a latency of 4.4 ms. Validation on public DeepPCB and NEU datasets further confirms DHNet's superiority, which can reach 99.1% and 79.9% mAP, respectively. Finally, successful deployment on the NVIDIA Jetson Nano platform validates DHNet's suitability for real-time defect detection in industrial applications.