PHOTOVOLTAIC MODULE SEGMENTATION BASED ON DEEP RESIDUAL ATTENTION NETWORK

Li Peng; Ning Hao; Su Yunlong; Meng Qingwei; Chen Jiming

doi:10.19912/j.0254-0096.tynxb.2024-1510

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (1) : 72-81. DOI: 10.19912/j.0254-0096.tynxb.2024-1510

PHOTOVOLTAIC MODULE SEGMENTATION BASED ON DEEP RESIDUAL ATTENTION NETWORK

Li Peng, Ning Hao, Su Yunlong, Meng Qingwei, Chen Jiming

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Abstract

To address the challenge of segmenting photovoltaic （PV） modules in remote sensing images, this paper proposes a semantic segmentation method based on a deep residual attention network. The method builds on the U-Net architecture by integrating a deep residual network for enhanced feature extraction and representation. Additionally, a local attention mechanism is incorporated within the residual modules to further refine local feature expression, improving segmentation accuracy. Experimental results on a public remote sensing PV dataset demonstrate that the proposed method consistently outperforms DeepLabv3+, UCTransNet, and UDTransNet across multiple spatial resolutions, achieving average improvements of 5.80%, 2.91%, 3.06% and 3.92% in mIoU, Dice, mPA, and Precision, respectively, over the original U-Net.

Key words

photovoltaic modules / deep learning / semantic segmentation / deep residual network / U-Net / attention mechanism

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Li Peng, Ning Hao, Su Yunlong, Meng Qingwei, Chen Jiming. PHOTOVOLTAIC MODULE SEGMENTATION BASED ON DEEP RESIDUAL ATTENTION NETWORK[J]. Acta Energiae Solaris Sinica. 2026, 47(1): 72-81 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1510

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