PHOTOVOLTAIC HOT SPOT DETECTION AND POSITIONING METHOD BASED ON LIGHTWEIGHT YOLOv5s

Sun Hairong; Liu Yongpeng; Zhou Lihui

doi:10.19912/j.0254-0096.tynxb.2023-1110

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (11) : 282-288. DOI: 10.19912/j.0254-0096.tynxb.2023-1110

PHOTOVOLTAIC HOT SPOT DETECTION AND POSITIONING METHOD BASED ON LIGHTWEIGHT YOLOv5s

Sun Hairong¹, Liu Yongpeng^1,2, Zhou Lihui³

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Abstract

A lightweight YOLOv5s based photovoltaic hot spot detection and localization method is proposed to address the issues of low model detection speed, complex computation, and complex model structure in current target detection technologies for detecting photovoltaic hot spot effects. Firstly, based on the YOLOv5s model, the lightweight network ShuffleNetV2 is introduced to improve the backbone network of YOLOv5s. By utilizing its design ideas of group convolution and channel shuffling, the model parameters and computational complexity are reduced while maintaining high accuracy. Secondly, introducing lightweight convolution GSConv to optimize the Neck part of YOLOv5s, utilizing its deep separable convolution combined with standard convolution to reduce computational complexity and optimize the overall model. Finally, use the dataset for validation. The results indicate that the lightweight model can improve detection speed, reduce parameter and computational complexity while ensuring high accuracy.

Key words

PV modules / feature extraction / infrared thermal imaging / image recognition / hot spot detection / YOLOv5

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Sun Hairong, Liu Yongpeng, Zhou Lihui. PHOTOVOLTAIC HOT SPOT DETECTION AND POSITIONING METHOD BASED ON LIGHTWEIGHT YOLOv5s[J]. Acta Energiae Solaris Sinica. 2024, 45(11): 282-288 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1110

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