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Related Experiment Video

Updated: Mar 15, 2026

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An Improved YOLOv8 Detection Algorithm Based on Screen Printing Defect Images.

Shuqin Wu1, Xinru Dong1, Qiang Da1,2

  • 1Beijing Key Laboratory of Digital Printing Equipment, School of Mechanical and Electrical Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary

This study introduces an improved YOLOv8 algorithm for detecting micro-defects in photovoltaic cells, enhancing accuracy and reducing errors in screen printing quality inspection.

Keywords:
YOLOv8deep learningdetectionmachine visionscreen printing defect

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computer Vision

Background:

  • Micro-defects like ink spots, scratches, and sintering in screen-printed photovoltaic cells degrade module performance.
  • Existing machine vision and deep learning methods struggle with accurate detection of small or background-similar defects in industrial settings.

Purpose of the Study:

  • To develop an enhanced defect detection methodology for photovoltaic cells using an improved YOLOv8 algorithm.
  • To improve the accuracy and robustness of detecting micro-defects in complex industrial environments.

Main Methods:

  • Developed a multi-focus image acquisition platform with primary and auxiliary CCDs, industrial camera, and electron microscope.
  • Optimized the YOLOv8 algorithm by integrating RepNCSPELAN4 module for feature fusion and a WaveConv module for detail preservation.
  • Incorporated a shuffle attention mechanism, detail enhancement module, and an auxiliary detection head for small defect detection.

Main Results:

  • The enhanced YOLOv8 model achieved a mean average precision of approximately 92% on a custom dataset.
  • Achieved 84.9% precision and 77.7% recall for ink spot detection, significantly reducing missed small defects.
  • Attained 98.9% precision and 100% recall for sintering defects, and 92.2% precision for scratch detection.

Conclusions:

  • The proposed methodology significantly enhances the accuracy and robustness of screen-printing defect detection in photovoltaic cells.
  • The optimized YOLOv8 algorithm provides an effective solution for real-time online quality inspection in smart manufacturing.