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Updated: Jul 8, 2026

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Lightweight PCB defect detection method based on SCF-YOLO.

Yazhou Li1, Yuanyuan Wang1, Jiange Liu2

  • 1College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China.

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|April 7, 2025
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This study introduces SCF-YOLO, a lightweight model for real-time printed circuit board (PCB) defect detection. It significantly reduces model size and boosts detection speed, making it ideal for resource-limited industrial applications.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Industrial Automation

Background:

  • Real-time defect detection in printed circuit boards (PCBs) faces challenges with large model sizes and slow speeds.
  • Resource limitations hinder the deployment of effective defect detection algorithms in industrial settings.

Purpose of the Study:

  • To develop a lightweight and efficient defect detection model for PCBs.
  • To address the limitations of large model size and slow inference speed in complex PCB inspection scenarios.

Main Methods:

  • Proposed SCF-YOLO model utilizing MobileNet as a lightweight feature extraction network.
  • Introduced a learnable weighted feature fusion module in the neck for multi-scale feature enhancement.
  • Developed a novel SCF (Synthesis C2f) module to improve high-level semantic feature capture.
  • Employed a combined CIoU and GIoU loss function for precise defect localization.

Main Results:

  • SCF-YOLO demonstrated a 25% reduction in parameters compared to YOLOv8.
  • Achieved up to a 60% improvement in detection speed over YOLOv8.
  • The model effectively enhances feature expression and focuses on key defect features.

Conclusions:

  • SCF-YOLO offers a fast, accurate, and efficient solution for PCB defect detection.
  • The lightweight design makes it suitable for deployment in resource-constrained industrial environments.
  • This advancement contributes to improved quality control in PCB manufacturing.