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PCB defect detection based on pseudo-inverse transformation and YOLOv5
Xiaoli Wang1,2, Siti Sarah Maidin2, Malathy Batumalay2
1Artificial Intelligence Department, Shanxi Polytechnic College, Taiyuan, China.
Plos One
|December 12, 2024
Summary
This study introduces an improved YOLOv5 method for printed circuit board (PCB) defect detection, enhancing image restoration and detection accuracy for complex integrated circuits.
Area of Science:
- Electrical Engineering
- Computer Vision
- Artificial Intelligence
Background:
- Integrated circuit packaging advancements lead to complex printed circuit board (PCB) layouts.
- Traditional defect detection methods lack the precision required for modern PCBs.
Purpose of the Study:
- To develop an efficient and accurate defect detection method for PCBs.
- To address the limitations of traditional methods in high-precision PCB inspection.
Main Methods:
- A defect image restoration model was developed to enhance image clarity.
- An improved YOLOv5 model incorporating Transformer was proposed, with optimized batch normalization and loss functions.
Main Results:
- The image restoration model showed a 37.60%-42.38% increase in restoration speed.
- The proposed PCB defect detection model achieved an average recall increase of 10.90% and average detection accuracy increase of 12.87%.
Conclusions:
- The developed method significantly improves image processing quality and YOLOv5 performance for PCB defect detection.
- The approach demonstrates superior effectiveness over existing technologies, with high accuracy (over 98.52%) for various defects.
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