SOLAR CELL DEFECT DETECTION BASED ON MAMBA AND CONVOLUTIONAL NEURAL NETWORKS

Qian Yao; Liu Gang; Zhao Long; Tang Jianchao

doi:10.19912/j.0254-0096.tynxb.2024-1988

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (3) : 690-696. DOI: 10.19912/j.0254-0096.tynxb.2024-1988

SOLAR CELL DEFECT DETECTION BASED ON MAMBA AND CONVOLUTIONAL NEURAL NETWORKS

Qian Yao¹, Liu Gang¹, Zhao Long², Tang Jianchao³

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Abstract

In order to address the problems of complex defect types and low efficiency of manual detection in solar cell electroluminescence images, a defect detection method based on Mamba and convolutional neural networks is proposed. The method overcomes the limitations of a single architecture in complex scenarios by fusing the local feature extraction capability of convolutional neural networks with the global information capture advantage of Mamba structures. The Mamba structure introduces a lightweight attention mechanism that dynamically adjusts the weights of features at different levels to enhance global information perception capability. Combined with convolutional neural networks, the model can both extract local detailed features and efficiently fuse global contextual information, thus significantly improving the accuracy and performance of defect detection. After a large amount of experiments, it is proved that the proposed method has the advantages of a low number of model’s parameters, high detection accuracy and strong robustness, which significantly improves the defect detection performance of solar cells.

Key words

solar cells / electroluminescence / deep learning / state space methods / defect detection / lightweighting

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Qian Yao, Liu Gang, Zhao Long, Tang Jianchao. SOLAR CELL DEFECT DETECTION BASED ON MAMBA AND CONVOLUTIONAL NEURAL NETWORKS[J]. Acta Energiae Solaris Sinica. 2026, 47(3): 690-696 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1988

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