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Solar panel defect detection design based on YOLO v5 algorithm.

Jing Huang1, Keyao Zeng1, Zijun Zhang1

  • 1School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, 350118, China.

Heliyon
|August 14, 2023
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Summary

This study enhances the YOLO v5 algorithm for solar panel defect detection, improving accuracy and efficiency to prevent electrical accidents. The optimized method ensures better quality control and electrical safety in solar energy systems.

Keywords:
Defect detectionElectrical safetySolar panelsYOLO v5

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Materials Science

Background:

  • Defects in solar panels pose significant electrical accident risks.
  • Traditional defect detection methods often suffer from low efficiency.
  • Ensuring solar panel quality is crucial for electrical safety and system performance.

Purpose of the Study:

  • To improve the detection efficiency and accuracy of solar panel defects.
  • To enhance the performance of the YOLO v5 algorithm for defect identification.
  • To contribute to standardized solar panel quality and electrical safety.

Main Methods:

  • Modified YOLO v5 algorithm incorporating a LCA attention mechanism for wider target feature sensing.
  • Implemented a weighted bidirectional feature pyramid for balanced multi-scale feature fusion.
  • Replaced the coupled head with a decoupled head for improved task-specific accuracy.

Main Results:

  • Achieved an overall precision increase of 1.5% and a recall rate increase of 2.4%.
  • The mean Average Precision (mAP) reached 95.5%, a 2.5% improvement over the original algorithm.
  • The enhanced algorithm demonstrated superior performance in identifying solar panel defects.

Conclusions:

  • The improved YOLO v5 algorithm significantly enhances solar panel defect detection accuracy and efficiency.
  • This advancement aids in standardizing solar panel quality and mitigating electrical accident risks.
  • The findings support the adoption of advanced AI techniques for ensuring safety and reliability in solar energy infrastructure.