Research Progress on Deep Learning Based Defect Detection Technology for Solar Panels

Authors

  • Yuxin Wang Tianjin Agricultural University image/svg+xml
  • Jiangyang Guo Tianjin Agricultural University image/svg+xml
  • Yifeng Qi Tianjin Agricultural University image/svg+xml
  • Xiaowei Liu Tianjin Agricultural University image/svg+xml
  • Jiangning Han Unicom Video Technology Co. LTD
  • Jialiang Zhang Tianjin Agricultural University image/svg+xml
  • Zhi Zhang Tianjin Agricultural University image/svg+xml
  • Jianguo Lian Tianjin Huada Technology Co
  • Xiaoju Yin Shenyang Institute of Technology

DOI:

https://doi.org/10.4108/ew.5740

Keywords:

Solar panels, Fault diagnosis, Deep learning, Defect detection, Machine learning

Abstract

INTRODUCTION: Based on machine vision technology to carry out photovoltaic panel defect detection technology research to solve the photovoltaic panel production line automation online defect detection and localization problems.

OBJECTIVES: The goal is to improve the accuracy of defect detection on PV cell production lines, increase the speed of defect detection to meet real-time monitoring needs, and improve production efficiency.

METHODS: In this paper, three detection methods such as image processing based detection, traditional machine learning based detection and deep learning algorithm based detection are discussed and compared and analyzed respectively. Finally, it is concluded that deep learning based detection methods are more effective in comparison. Then, further analysis and simulation experiments are done by several deep learning based detection algorithms.

RESULTS: The experimental results show that the YOLOv8 algorithm has the highest precision rate and maintains good results in terms of recall and mAP values. The detection speed is all less than other algorithms, 10.6ms.

CONCLUSION: The inspection model based on yolov8 algorithm has the highest comprehensive performance and is the most suitable algorithmic model for detecting defects in solar panels in production lines.

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Published

11-04-2024

How to Cite

1.
Wang Y, Guo J, Qi Y, Liu X, Han J, Zhang J, Zhang Z, Lian J, Yin X. Research Progress on Deep Learning Based Defect Detection Technology for Solar Panels. EAI Endorsed Trans Energy Web [Internet]. 2024 Apr. 11 [cited 2024 May 20];11. Available from: https://publications.eai.eu/index.php/ew/article/view/5740