Research Article
Research Progress on Deep Learning Based Defect Detection Technology for Solar Panels
@ARTICLE{10.4108/ew.5740, author={Yuxin Wang and Jiangyang Guo and Yifeng Qi and Xiaowei Liu and Jiangning Han and Jialiang Zhang and Zhi Zhang and Jianguo Lian and Xiaoju Yin}, title={Research Progress on Deep Learning Based Defect Detection Technology for Solar Panels}, journal={EAI Endorsed Transactions on Energy Web}, volume={11}, number={1}, publisher={EAI}, journal_a={EW}, year={2024}, month={4}, keywords={Solar panels, Fault diagnosis, Deep learning, Defect detection, Machine learning}, doi={10.4108/ew.5740} }- Yuxin Wang
Jiangyang Guo
Yifeng Qi
Xiaowei Liu
Jiangning Han
Jialiang Zhang
Zhi Zhang
Jianguo Lian
Xiaoju Yin
Year: 2024
Research Progress on Deep Learning Based Defect Detection Technology for Solar Panels
EW
EAI
DOI: 10.4108/ew.5740
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.
Copyright © 2024 Y. Wang et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
