Electroluminescence defect image augmentation method of solar cell based on cycleGAN

HE xiang; YANG aijun; LI jiansheng; CHEN caiyun; YOU hongliang

doi:10.37188/CJLCD.2023-0234

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Electroluminescence defect image augmentation method of solar cell based on cycleGAN

更新时间：2023-08-23

- Electroluminescence defect image augmentation method of solar cell based on cycleGAN
- Chinese Journal of Liquid Crystals and Displays Pages: 1-14(2023)
- 作者机构：
  
  福建省计量科学研究院国家光伏产业计量测试中心，福建福州 350003福建省计量科学研究院福建省能源计量重点实验室，福建福州 350003
- 作者简介：
- 基金信息：
  
  State Administration for Market Regulation of China(2021MK050)
- DOI：10.37188/CJLCD.2023-0234
  CLC： TN206
- Published Online：23 August 2023，
  
  Received：04 July 2023，
  
  Revised：31 July 2023，
- 稿件说明：
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HE xiang, YANG aijun, LI jiansheng, et al. Electroluminescence defect image augmentation method of solar cell based on cycleGAN. [J/OL]. Chinese Journal of Liquid Crystals and Displays 1-14(2023)
DOI：

HE xiang, YANG aijun, LI jiansheng, et al. Electroluminescence defect image augmentation method of solar cell based on cycleGAN. [J/OL]. Chinese Journal of Liquid Crystals and Displays 1-14(2023) DOI： 10.37188/CJLCD.2023-0234.

摘要

为了解决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，并且将生成的图像与具有代表性的DCGAN所生成的图像进行了对比。将拍摄到的EL图像进行分类、进行数据增强以形成训练集。接着采用训练集训练两个模型。最后，从生成图像的有效性，相似性，多样性三个角度对比了两个模型所生成图像。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所生成的图像感官相似性极高，不存在 “脏污感”和“粗糙感”，难以通过人眼分辨。在FID指标上，cycleGAN所生成的图像显著低于DCGAN；采用cycleGAN生成图像训练的分类模型中，对真实拍摄的图像组成的测试集准确率达到93.45%，当训练集中混入少量真实拍摄的图像时，该准确率提升至到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，多样性三个面显著优于DCGAN。

Abstract

In order to solve the problem of insufficient training images and poor quality of generated images in the automatic recognition research of electroluminescence defects （EL） in photovoltaic modules， The solar cell EL defect images are generated by using the cycleGAN， and the generated images is compared with the images generated by the representative DCGAN. Classify the EL images and perform data augmentation to form a training set. Next， cycleGAN and DCGAN were trained using training set. Finally， a detailed comparison was 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 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. And there is no “dirty” or “rough” phenomenon. For FID indicators， the images generated by cycleGAN are significantly lower than those generated by DCGAN. The classification model trained with images generated by cycleGAN achieved a 93.45% accuracy rate on the test set composed of captured EL images. When a small number of captured EL images were 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 is significantly lower than that of DCGAN. Using cycleGAN for data augmentation of solar cell EL images is an effective method. And it performs significantly better than DCGAN in terms of effectiveness， similarity， and diversity.

关键词

光伏组件太阳电池电致发光cycleGANDCGAN

Keywords

Photovoltaics moduleSolar cellsElectroluminescencecycleGANDCGAN

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