Solar Cell Defect Generation Algorithm Combining Multiple Perception Fields and Attention
ZHOU Ying1,2, PEI Shenghu1, CHEN Haiyong1,2, YAN Yuze1
1. School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401; 2. Hebei Control Engineering Technology Research Center, Tianjin 300130
Abstract：Aiming at the problem of insufficient image samples for some certain defects in solar cells, a solar cell defect generation algorithm combining multiple perception fields and attention is proposed. The generated images are utilized to train the defect detection model. Firstly, a generative adversarial network with dual discriminators is constructed, and a global discriminator and a local discriminator focuse on global information and local details, respectively. Secondly, the multiple perception field feature extraction is designed and fused with the improved attention module to form a multiple perception field attention module. The module is utilized in the network structure of both the generator and the discriminator. Finally, structural similarity loss and peak signal-to-noise ratio loss are added to the loss function for generator training, and the generated images are mean filtered. The generation experiments for 3 different scales of defect images on the solar electroluminescence dataset show that the structural similarity and peak signal-to-noise ratio are high. Additionally, after training the YOLOv7 detection model with the generated defect images, the average precision values for all three defects are high.
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2023, Vol. 36 
