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A Novel PCB Surface Defect Detection Method Based on the GBE-YOLOv8 Model
Chao Gao1, Xin Zhang1, Mengting Bai1
1School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 102488, China.
Micromachines
|March 28, 2026
Summary
A new Ghost-BiFPN-Efficient-YOLOv8 (GBE-YOLOv8) model enhances printed circuit board (PCB) defect detection. This AI approach improves accuracy and efficiency for real-time surface inspection in manufacturing.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Manufacturing Technology
Background:
- Surface defect detection is crucial for printed circuit board (PCB) quality and safety.
- Real-time detection of tiny PCB defects is challenging due to complex layouts.
Purpose of the Study:
- To develop a novel and efficient model for PCB surface defect detection.
- To improve the accuracy and computational efficiency of existing object detection models for PCB inspection.
Main Methods:
- Proposed a Ghost-BiFPN-Efficient-YOLOv8 (GBE-YOLOv8) model based on YOLOv8n.
- Incorporated lightweight Ghost Conv, G-C2f, BiFPN-Concat, and Efficient Head modules.
- Analyzed model interpretability using class activation heatmaps.
Main Results:
- Achieved high performance with mAP@0.5 of 98.9% and mAP@0.5:0.95 of 61.4%.
- Demonstrated reduced computational complexity (2.6 M parameters, 7.5 GFLOPs) and high speed (252 FPS).
- Outperformed baseline and state-of-the-art object detection algorithms.
Conclusions:
- The GBE-YOLOv8 model offers a significant advancement in PCB surface defect detection.
- The proposed optimizations effectively balance detection accuracy and computational efficiency.
- Provides a reliable technical solution for high-precision, real-time PCB inspection in industrial settings.
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