基于cycleGAN的太阳电池电致发光图像数据增强方法

Electroluminescence defect image augmentation method of solar cell based on cycleGAN

针对光伏组件电致发光缺陷自动识别研究中训练用图像不足以及生成图像质量不佳的问题,采用cycleGAN生成了太阳电池EL缺陷图像,并与DCGAN生成的图像进行了综合性对比。将拍摄到的EL图像进行分类和数据增强形成训练集。接着采用训练集对cycleGAN与DCGAN进行训练。最后,从生成图像的有效性、相似性和多样性3个角度对比了两个模型所生成的图像。实验结果表明,cycleGAN所生成的图像中,有效图像的占比显著高于DCGAN。与真实拍摄的图像相比,cycleGAN所生成图像的感官相似性极高,难以通过人眼分辨。cycleGAN所生成图像的FID指标显著低于DCGAN。采用cycleGAN生成图像训练的分类模型对真实拍摄图像的准确率达到93.45%,当训练集中混入少量真实拍摄图像时,该准确率提升至98.26%,显著高于DCGAN。cycleGAN生成图像的平均MS-SSIM指标显著低于DCGAN。采用cycleGAN进行太阳电池EL图像的数据增强是一种行之有效的方法,在有效性、相似性和多样性3方面显著优于DCGAN。

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.