Photovoltaic defect detection method for marine pasture based on deep learning

Dan Guo; Xueming Qiao; Ming Xu; Ping Meng; Qun Yong; Yuwen Li; Shuangchao Li

doi:10.1117/12.2674967

2 May 2023 Photovoltaic defect detection method for marine pasture based on deep learning

Dan Guo, Xueming Qiao, Ming Xu, Ping Meng, Qun Yong, Yuwen Li, Shuangchao Li

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126420Q (2023) https://doi.org/10.1117/12.2674967
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

As a large-scale marine fishery facility, marine ranch plays an important role. At the same time, in the current context of energy conservation and environmental protection, solar energy has the characteristics of clean, environmental protection, high efficiency and easy access, so it undertakes the important task of energy consumption control and energy conservation and environmental protection. However, under the influence of human and natural factors, solar panels inevitably have cracks, shielding and other defects. If it cannot be found and maintained in time, it will seriously affect the power supply efficiency and energy consumption control. Therefore, it is extremely critical to accurately detect the defects of the photovoltaic system in the marine pasture. Aiming at the problems of high false detection rate, unbalanced detection speed and accuracy in the original photovoltaic defect detection method, this paper proposes an improved method based on YOLOv5s. This method embeds CA (Coord Attention) attention mechanism in the backbone network, and uses BiFPN (Bidirectional Feature Pyramid Network) to replace the original PANet to improve the feature fusion ability. The final experimental results show that the precision of the proposed method is 1.2% higher than that of the original algorithm, and the amount of parameters and computation are reduced by 25.3% and 16.5% respectively, and the reasoning speed is the same. Therefore, this method achieves the balance between speed and accuracy, and the reduction of model size also provides the possibility for its further deployment.

Citation Download Citation

Dan Guo, Xueming Qiao, Ming Xu, Ping Meng, Qun Yong, Yuwen Li, and Shuangchao Li "Photovoltaic defect detection method for marine pasture based on deep learning", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126420Q (2 May 2023); https://doi.org/10.1117/12.2674967

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KEYWORDS

Defect detection

Photovoltaics

Oceanography

Data modeling

Detection and tracking algorithms

Solar energy

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