FAGAN ： AN ADVERSARIAL GENERATION METHOD OF SOLAR CELLS DEFECT IMAGE BASED ON MODEL TRANSFER AND ATTENTION MECHANISM

Sun Lingjun; Mao Jingyu; Liu Kun

doi:10.19912/j.0254-0096.tynxb.2022-0754

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (9) : 78-84. DOI: 10.19912/j.0254-0096.tynxb.2022-0754

FAGAN ： AN ADVERSARIAL GENERATION METHOD OF SOLAR CELLS DEFECT IMAGE BASED ON MODEL TRANSFER AND ATTENTION MECHANISM

Sun Lingjun¹, Mao Jingyu², Liu Kun¹

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Abstract

Small and weak defects and insufficient dataset are the bottlenecks problems which restrict the rapid development of solar cells quality detection technology. Therefore, this paper proposes FAGAN for generating defect images of solar cells. This method firstly performs model pre-training on the source domain open road dataset to extract cross-domain underlying visual features, so as to improve the diversity of defect forms generated by FAGAN on the target domain; then, ECSA is designed to enhance the defect features in two dimensions of space and channel, so as to improve the quality of small and weak defect samples. The experiments show that the FID of the solar cell defect images generated by the proposed method is better than those of the existing gradient penalty Wasserstein distance generative adversarial network （WGAN-GP）, deep convolution generative adversarial network （DCGAN）, cycle generative adversarial network （CycleGAN） and style generative adversarial network （StyleGAN）.

Key words

transfer learning / solar cells / image processing / convolutional neural networks / generative adversarial network / attention

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Sun Lingjun, Mao Jingyu, Liu Kun. FAGAN ： AN ADVERSARIAL GENERATION METHOD OF SOLAR CELLS DEFECT IMAGE BASED ON MODEL TRANSFER AND ATTENTION MECHANISM[J]. Acta Energiae Solaris Sinica. 2023, 44(9): 78-84 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0754

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