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

Yuxin Wang; Jiangyang Guo; Yifeng Qi; Xiaowei Liu; Jiangning Han; Jialiang Zhang; Zhi Zhang; Jianguo Lian; Xiaoju Yin

doi:10.4108/ew.5740

Authors

Yuxin Wang Tianjin Agricultural University
Jiangyang Guo Tianjin Agricultural University
Yifeng Qi Tianjin Agricultural University
Xiaowei Liu Tianjin Agricultural University
Jiangning Han Unicom Video Technology Co. LTD
Jialiang Zhang Tianjin Agricultural University
Zhi Zhang Tianjin Agricultural University
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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