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PCB defect detection algorithm based on CDI-YOLO.

Gaoshang Xiao1, Shuling Hou1, Huiying Zhou2

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This study introduces CDI-YOLO, an efficient algorithm for detecting printed circuit board (PCB) defects. The novel method enhances accuracy and speed while reducing model parameters for improved PCB manufacturing quality control.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Printed circuit board (PCB) manufacturing is susceptible to defects impacting performance and reliability.
  • Current deep learning methods for PCB defect detection struggle to balance accuracy, speed, and model size.

Purpose of the Study:

  • To develop an advanced PCB defect detection algorithm that overcomes limitations of existing methods.
  • To improve the accuracy, detection speed, and efficiency of PCB defect identification.

Main Methods:

  • A novel algorithm, CDI-YOLO, is proposed, built upon YOLOv7-tiny.
  • Incorporates coordinate attention mechanism (CA) to boost feature extraction.
  • Utilizes DSConv for reduced computational cost and faster detection.
  • Employs Inner-CIoU as the bounding box regression loss for accelerated localization.

Main Results:

  • Achieved 98.3% mean Average Precision (mAP) on a PCB defect dataset.
  • Demonstrated a detection speed of 128 frames per second (FPS).
  • Maintains a compact model size with 5.8 million parameters and 12.6 Giga floating-point operations per second (GFLOPs).

Conclusions:

  • The proposed CDI-YOLO algorithm offers superior comprehensive performance compared to existing methods.
  • This approach provides an effective solution for high-accuracy, high-speed PCB defect detection.
  • The method contributes to enhancing the quality and reliability of manufactured PCBs.