MULTI-SOURCE IMAGE FUSION METHODS FOR PHOTOVOLTAIC MODULE DEFECT DETECTION

Chen Dengfeng; Liu Qianqian; Liu Shipeng; Xiao Haiyan

doi:10.19912/j.0254-0096.tynxb.2024-2110

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (4) : 622-629. DOI: 10.19912/j.0254-0096.tynxb.2024-2110

MULTI-SOURCE IMAGE FUSION METHODS FOR PHOTOVOLTAIC MODULE DEFECT DETECTION

Chen Dengfeng¹, Liu Qianqian¹, Liu Shipeng², Xiao Haiyan³

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Abstract

To address the limitations of accuracy and comprehensiveness in single-image defect detection for photovoltaic modules, a multi-source image fusion method is proposed for detecting defects in photovoltaic modules. Firstly, the Cross-Modulation is embedded in the encoder of the image fusion network. Feature alignment is used instead of image registration to reveal the correspondence between the features of visible light and infrared images of photovoltaic modules, enabling the fusion of unregistered images. Secondly, the Skip Mechanism is adopted to selectively apply the attention mechanism, accelerating image processing and reducing resource consumption. Finally, the fused images are utilized for detection using the YOLO series defect detection networks. Experimental results indicate that the proposed multi-source image fusion method effectively overcomes the limitations of single-image defect detection, resulting in an average precision improvement of 5.6% across multiple networks.

Key words

photovoltaic modules / visible images / infrared images / image fusion / defect detection

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Chen Dengfeng, Liu Qianqian, Liu Shipeng, Xiao Haiyan. MULTI-SOURCE IMAGE FUSION METHODS FOR PHOTOVOLTAIC MODULE DEFECT DETECTION[J]. Acta Energiae Solaris Sinica. 2026, 47(4): 622-629 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2110

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