Solar Cell Defect Image Generation Based on Dual-Dimensional Attention Integrated Adversarial Network
Aiming at the problem of scarcity of solar cell defect images,a dual-dimensional attention inte-grated adversarial network for defect images generation is proposed for solar cell defect detection model training.Firstly,an integrated adversarial network model with dual generators and dual discriminators is constructed;secondly,channel attention and improved spatial attention are combined into dual-dimensional attention which is incorporated into generators and discriminators;finally,for solving unstable problems when training model,a dual-generator time-sharing training approach is designed.Compared with the exist-ing optimal generation methods on the solar cell electroluminescence(EL)defect dataset,the Fréchet incep-tion distance(FID)and structural similarity index measure(SSIM)of the five kinds generated defect images are increased by 53.87 and 0.46.In addition,the mean average precision(MAP)of the five kinds defect im-ages reaches 96.56％by using the generated defect images to train the yolov5 detection model.
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