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A Novel YOLOv5_ES based on lightweight small object detection head for PCB surface defect detection.

Yi Gao1, Zhensong Li2, Yutong Wang1

  • 1Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, 100192, China.

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|October 9, 2024
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Summary

This study introduces an improved YOLOv5 model for printed circuit board (PCB) defect detection, enhancing accuracy and efficiency for small defects. The new method significantly reduces model size and computational cost while improving defect identification capabilities.

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Area of Science:

  • * Manufacturing Engineering
  • * Computer Vision
  • * Artificial Intelligence

Background:

  • * Surface defects in printed circuit board (PCB) manufacturing critically impact product quality.
  • * Traditional object detection algorithms struggle with complex backgrounds, diverse defect types, and small-sized defects in PCB inspection.
  • * Existing methods often lack the accuracy and efficiency required for real-world PCB manufacturing environments.

Purpose of the Study:

  • * To develop a highly accurate and efficient PCB defect detection algorithm for improved manufacturing quality control.
  • * To address the limitations of traditional object detection methods in identifying small and complex defects on PCBs.
  • * To create a lightweight yet powerful model for real-time PCB surface inspection.

Main Methods:

  • * Proposed a novel YOLOv5 defect detection algorithm (YOLOv5_ES) incorporating a multi-scale attention mechanism (EMA) and spatial pyramid dilated convolution (SPD-Conv).
  • * Optimized the YOLOv5s framework by modifying the detection head to focus on small detection layers, enhancing the identification of minor defects.
  • * Integrated SPD-Conv to reduce information loss during feature extraction and EMA to fuse multi-scale context information, improving generalization.

Main Results:

  • * Achieved a 3.1% improvement in mean average precision (mAP0.5) compared to the standard YOLOv5s model.
  • * Reduced model parameters by 55.8%, leading to a more lightweight and efficient model.
  • * Decreased Giga Floating-point Operations Per Second (GFLOPs) by 4.8%, indicating reduced computational cost.

Conclusions:

  • * The proposed YOLOv5_ES model demonstrates superior performance in PCB defect detection, particularly for small and complex defects.
  • * Significant reductions in model parameters and computational load were achieved without compromising accuracy.
  • * The enhanced model offers a promising solution for accurate, efficient, and lightweight PCB surface inspection in manufacturing.