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September 5, 2025Scientific ReportsOpen Access

Advanced deep learning modeling to enhance detection of defective photovoltaic cells in electroluminescence images

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The pre-trained ResNet152 model achieved a peak classification accuracy of 90.13% for defect detection.
Using generative adversarial networks helps create high-quality electroluminescence images to mitigate dataset imbalance.
Implementing a one-cycle policy with optimized learning rates enhances training efficiency and accelerates performance.
Over-sampling approaches significantly improve accuracy and robustness in detecting defects in photovoltaic modules.

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This approach demonstrates improved detection accuracy in photovoltaic cells using deep learning and generative adversarial networks.

Ebied et al. (2025) studied this question.

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