HEAT SPOT DETECTION METHOD OF PHOTOVOLTAIC INFRARED IMAGE BASED ON DEEP LEARNING

Niu Xiaoyu; Liu Changliang; Liu Weiliang; Liu Shuai; Wang Xin; Kang Jiayao

doi:10.19912/j.0254-0096.tynxb.2023-1057

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (11) : 272-281. DOI: 10.19912/j.0254-0096.tynxb.2023-1057

HEAT SPOT DETECTION METHOD OF PHOTOVOLTAIC INFRARED IMAGE BASED ON DEEP LEARNING

Niu Xiaoyu¹, Liu Changliang^1,2, Liu Weiliang^1,2, Liu Shuai^1,2, Wang Xin³, Kang Jiayao³

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Abstract

Heat spot faults in aerial photovoltaic infrared images are mostly small targets, which are very similar to the interference background, resulting in low detection accuracy of heat spot faults. A second-order segment heat spot detection method based on deep learning is proposed. In the first stage, interference background removal is performed. Aiming at the problems of slow segmentation speed, poor edge extraction effect and unbalanced positive and negative samples of photovoltaic modules, an improved DeepLabv3+ segmentation model was proposed by replacing the backbone network and adopting hybrid loss function to achieve fast and accurate extraction of photovoltaic module regions. In the second stage, the hot spot fault detection is carried out. To solve the problem of missing and false detection of small target hot spots, an improved YOLOv5 hot spot detection model was proposed by adopting the enhanced SPP module, introducing shallow detection scale and changing the border regression loss function, so as to achieve accurate identification of hot spots. The results show that compared with the original DeepLabv3+ segmentation model, the average pixel accuracy and average crossover ratio of the proposed segmentation model are increased by 1.7 and 1.51 percentage points, respectively. Compared with the original YOLOv5 model, the average accuracy of mAP₅₀ and mAP_50∶95 of the proposed heat spot detection model is increased by 2.6 and 10.7 percentage paints, respectively.

Key words

PV modules / infrared imagery / hot spot / deep learning / DeepLabv3+ / YOLOv5

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Niu Xiaoyu, Liu Changliang, Liu Weiliang, Liu Shuai, Wang Xin, Kang Jiayao. HEAT SPOT DETECTION METHOD OF PHOTOVOLTAIC INFRARED IMAGE BASED ON DEEP LEARNING[J]. Acta Energiae Solaris Sinica. 2024, 45(11): 272-281 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1057

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