Improved DCGAN for Solar Cell Defect Enhancement

Abstract

Aiming at the problems of serious overfitting and poor training results caused by too small a data set of solar cell defect images in the process of deep learning training, an improved DCGAN generation countermeasure network model is proposed. Firstly, CLAHE preprocessing is used to enhance the defect image features, which can improve the defect contrast and avoid excessive noise enhancement at the same time; Secondly, the NAM attention module is introduced into DCGAN to improve the quality of the defect image; Finally, S-RELU is used to replace Leaky Relu in DCGAN discriminator to avoid the influence of too much negative information with gradient on the decision of discriminator. The experimental results of classification and detection show that the data enhancement effect of the improved model is better. Compared with the original model, its accuracy is improved by 2.51%, and the mapped value is improved by 1.92%, which proves the effectiveness of the proposed algorithm.