基于cycleGAN的太阳电池电致发光图像数据增强方法OA北大核心CSTPCD

In order to solve the problems of insufficient training images and poor quality of generated images in the automatic recognition research of electroluminescence(EL)defects in photovoltaic modules,the solar cell EL defect images are generated by using the cycleGAN,and the generated images are compared with the images generated by the representative DCGAN.The captured EL images are classified and performed data augmentation to form a training set.Next,cycleGAN and DCGAN are trained using training set.Finally,a detailed comparison is made between the generated images of the two models from three perspectives:effectiveness,similarity and diversity.The experimental results show that the proportion of effective images generated by cycleGAN is significantly higher than that of images generated by DCGAN.Compared with captured EL images,the images generated by cycleGAN have extremely high sensory similarity,making it difficult to distinguish them through the human eye.The FID indicators of the images generated by cycleGAN are significantly lower than images generated by DCGAN.The classification model trained with images generated by cycleGAN achieves a 93.45%accuracy rate on the test set composed of captured EL images.When a small number of captured EL images are included in the training dataset,the accuracy is improved to 98.26%,significantly higher than that of DCGAN.Finally,the average MS-SSIM indicators of images generated by cycleGAN are significantly lower than that of DCGAN.The use of cycleGAN is an effective method for data augmentation of solar cell EL images,which is significantly superior to DCGAN in terms of effectiveness,similarity and diversity.