SEMANTIC SEGMENTATION MODEL OF PHOTOVOLTAIC MODULES BASED ON VISUAL FEATURES

Wang Yin; Shen Lingxin; Li Maohuan; Wang Jian'an; Li Xiaosong

doi:10.19912/j.0254-0096.tynxb.2023-0265

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (4) : 500-511. DOI: 10.19912/j.0254-0096.tynxb.2023-0265

SEMANTIC SEGMENTATION MODEL OF PHOTOVOLTAIC MODULES BASED ON VISUAL FEATURES

Wang Yin¹, Shen Lingxin¹, Li Maohuan², Wang Jian'an¹, Li Xiaosong¹

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Abstract

To solve the segmentation problem of infrared images of photovoltaic modules, MobileNetv2 is used as the backbone feature extraction network of DeepLabv3+ and the location channel attention module is used to reduce background interference. Mixed strip pooling is introduced to optimize the ASPP module, which helps the model to further capture global and contextual information. The DeepLabv3-T network is designed for rooftop PV modules with difficult detection. Based on the above improvements, texture information is incorporated into the selective background suppression to achieve accurate segmentation of PV modules. Experimental results on the PV_large and PV_roof datasets demonstrate that the text-based approach is superior to the prior art, and the mIoU value of deeplabv3-t is 2.74% and 7.93% higher than that of DeepLabv3+, respectively. In addition, ablation experiments are designed to demonstrate the effectiveness of each improved module.

Key words

photovoltaic modules / semantic segmentation / deep learning / image texture / deeplab / attention mechanism

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Wang Yin, Shen Lingxin, Li Maohuan, Wang Jian'an, Li Xiaosong. SEMANTIC SEGMENTATION MODEL OF PHOTOVOLTAIC MODULES BASED ON VISUAL FEATURES[J]. Acta Energiae Solaris Sinica. 2024, 45(4): 500-511 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0265

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