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Local and Global Context-Enhanced Lightweight CenterNet for PCB Surface Defect Detection.

Weixun Chen1,2, Siming Meng1,2, Xueping Wang3

  • 1The Information Engineering Institute, Guangzhou Railway Polytechnic, Guangzhou 510430, China.

Sensors (Basel, Switzerland)
|July 27, 2024
PubMed
Summary

This study introduces LGCL-CenterNet, a lightweight model for real-time printed circuit board (PCB) surface defect detection. It enhances accuracy and speed by combining local and global context features, improving manufacturing quality.

Keywords:
CenterNetPANetPCB surface defect detectionlightweightingtwo-branch

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Manufacturing Technology

Background:

  • Printed circuit board (PCB) surface defect detection is critical for manufacturing quality.
  • Existing computer vision methods for PCB defect detection are computationally intensive and inefficient.
  • High-resolution imaging is available, but efficient analysis remains a challenge.

Purpose of the Study:

  • To develop a lightweight and efficient model for real-time PCB surface defect detection.
  • To improve the accuracy and inference speed of automated optical inspection (AOI) systems.
  • To address the computational limitations of current computer vision approaches in PCB manufacturing.

Main Methods:

  • Proposed a local and global context-enhanced lightweight CenterNet (LGCL-CenterNet).
  • Introduced a two-branch lightweight vision transformer module (LGT) with local (coordinate attention) and global (Bi-Level Routing Attention) attention mechanisms.
  • Incorporated a Path Aggregation Network (PANet) for feature fusion and a lightweight prediction head using depthwise separable convolutions.

Main Results:

  • LGCL-CenterNet achieved improved mAP@0.5 by 2% and 1.4% compared to CenterNet-ResNet18 and YOLOv8s, respectively.
  • The model requires fewer parameters (0.542M) than existing techniques.
  • Demonstrated superior detection accuracy and inference speed.

Conclusions:

  • LGCL-CenterNet offers enhanced real-time performance and robustness for PCB surface defect detection.
  • The proposed method effectively balances detection accuracy and computational efficiency.
  • This approach contributes to more efficient and reliable automated inspection in PCB manufacturing.