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浙江大学学报(工学版)
浙江大学学报(工学版)  2020, Vol. 54 Issue (4): 684-693    DOI: 10.3785/j.issn.1008-973X.2020.04.007
计算机技术、信息工程     
基于生成对抗网络的太阳能电池缺陷增强方法
刘坤1(),文熙1,黄闽茗2,杨欣欣1,毛经坤3
1. 河北工业大学 人工智能与数据科学学院，天津 300131
2. 贵州大学 大数据与信息工程学院，贵州 贵阳 550025
3. 天津芯思科技有限公司，天津 300450
Solar cell defect enhancement method based on generative adversarial network
Kun LIU1(),Xi WEN1,Min-ming HUANG2,Xin-xin YANG1,Jing-kun MAO3
1. College of Artificial Intelligence, Hebei University of Technology, Tianjin 300131, China
2. College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
3. Tianjin Xinsi Technology Company with Limited Liability, Tianjin 300450, China
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摘要：

为了解决太阳能电池样本不均衡问题，提出负样本引导生成对抗网络的太阳能电池缺陷样本增强方法. 通过在生成对抗模型中引入大量负样本和增加负样本引导损失，促进模型对正样本特征的表达，提升生成样本的多样性；设计自适应的权值约束方法，平衡生成器和判别器的表达能力，提升生成样本的质量. 实验结果表明，在太阳能电池电致发光(EL)缺陷数据集上，提出方法的生成质量和检测精度优于深度卷积生成对抗网络（DCGAN）、梯度惩罚Wasserstein距离生成对抗网络（WGAN-GP）和一阶导数生成对抗网络（FOGAN）；该方法的F测度较DCGAN、WGAN-GP和FOGAN分别最高提升了10%、8%和5%，具有较好的数据增强性能. 在带钢表面缺陷数据集及DAGM 2007公共数据集上，提出方法的性能优于DCGAN、WGAN-GP和FOGAN，具有一定的泛化能力.
关键词： 样本不均衡;  数据增强;  生成对抗网络（GAN）;  太阳能电池;  负样本引导    
Abstract:

A solar cells defect sample enhancement method which is negative sample-guided generative adversarial network was proposed in order to solve the problem of sample imbalance of solar cells. The representation ability of positive samples features was promoted and the diversity of generated samples was improved by introducing many negative samples and mixing negative sample guidance loss in the generative adversarial network. An adaptive weight constraint method was designed to balance the representation ability of generators and discriminators, and the quality of generated samples was improved. The experimental results show that the proposed method outperforms deep convolutional generative adversarial network (DCGAN), Wasserstein generative adversarial network-gradient penalty (WGAN-GP) and first-order generative adversarial network (FOGAN) in generation quality and detection accuracy on electroluminescence (EL) defect data sets of solar cells. F-measure of the method was 10%, 8% and 5% higher than DCGAN, WGAN-GP and FOGAN respectively, which showed better data enhancement performance. The performance of the proposed method is better than DCGAN, WGAN-GP and FOGAN on strip steel surface defect dataset and DAGM 2007 public dataset, which shows certain generalization ability.
Key words: data imbalance    data enhancement    generative adversarial network (GAN)    solar cell    negative sample guidance
收稿日期: 2019-11-10 出版日期: 2020-04-05
CLC:  TP 391  
基金资助: 国家自然科学基金资助项目（61403119，61203275）；河北省自然科学基金资助项目（F2018202078，F2019202305）
作者简介: 刘坤（1980─），女，副教授，从事机器视觉与智能控制研究. orcid.org/0000-0003-0774-3635. E-mail： liukun@hebut.edu.cn
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引用本文:

刘坤,文熙,黄闽茗,杨欣欣,毛经坤. 基于生成对抗网络的太阳能电池缺陷增强方法[J]. 浙江大学学报(工学版), 2020, 54(4): 684-693.

Kun LIU,Xi WEN,Min-ming HUANG,Xin-xin YANG,Jing-kun MAO. Solar cell defect enhancement method based on generative adversarial network. Journal of ZheJiang University (Engineering Science), 2020, 54(4): 684-693.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.04.007    或    http://www.zjujournals.com/eng/CN/Y2020/V54/I4/684

图 1  NSGGAN模型结构
图 2  不同样本的特征可视化
图 3  太阳能电池EL数据
图 4  不同数量负样本引导的F-measure曲线
图 5  DCGAN、WGAN-GP、FOGAN和NSGGAN生成太阳能电池EL缺陷样本
方法 s p1-NN MMD WD
DCGAN 20 0.81 0.35 4.3
DCGAN 50 0.79 0.33 4.0
DCGAN 100 0.76 0.31 3.8
DCGAN 200 0.75 0.30 3.6
WGAN-GP 20 0.73 0.32 4.2
WGAN-GP 50 0.73 0.31 3.9
WGAN-GP 100 0.72 0.28 3.7
WGAN-GP 200 0.69 0.27 3.5
FOGAN 20 0.68 0.29 3.8
FOGAN 50 0.65 0.25 3.4
FOGAN 100 0.61 0.20 3.1
FOGAN 200 0.58 0.18 2.9
NSGGAN 20 0.53 0.20 3.3
NSGGAN 50 0.53 0.18 3.2
NSGGAN 100 0.53 0.17 3.1
NSGGAN 200 0.52 0.15 2.8
表 1  各模型在太阳能电池EL缺陷数据上的生成质量
方法 GAN训练集 CNN训练集 CNN测试集
s n s n s n
没有数据增强 ? ? s 10 000 300?s 6 000
传统数据增强方法 s ? 10 000 10 000 300?s 6 000
DCGAN增强方法 s ? 10 000 10 000 300?s 6 000
WGAN-GP增强方法 s ? 10 000 10 000 300?s 6 000
FOGAN增强方法 s ? 10 000 10 000 300?s 6 000
NSGGAN增强方法 s 20 000 10 000 10 000 300?s 6 000
表 2  太阳能电池EL样本集
图 6  不同数量正样本下各方法在太阳能电池EL数据集的F测度曲线
对比方法 F
DCGAN 0.85
DCGAN + NSG 0.89
DCGAN + clip 0.91
DCGAN + adaptive_clip 0.92
DCGAN+NSG + clip 0.93
NSGGAN 0.96
表 3  各方法在太阳能电池EL数据集的F测度
对比方法 p1-NN MMD WD
DCGAN 0.81 0.35 4.3
DCGAN + NSG 0.75 0.29 3.8
DCGAN + clip 0.74 0.27 3.5
DCGAN +adaptive_clip 0.71 0.25 3.5
DCGAN + NSG+ clip 0.69 0.22 3.3
NSGGAN 0.53 0.18 3.1
表 4  各方法在太阳能电池EL缺陷数据集上的生成质量
图 7  DCGAN、WGAN-GP、FOGAN和NSGGAN生成的样本
方法 s p1-NN MMD WD
DCGAN 20 0.83 0.42 5.1
DCGAN 50 0.80 0.40 4.9
DCGAN 100 0.78 0.37 4.5
DCGAN 200 0.76 0.36 4.3
WGAN-GP 20 0.78 0.41 4.7
WGAN-GP 50 0.76 0.40 4.5
WGAN-GP 100 0.75 0.39 4.4
WGAN-GP 200 0.73 0.36 4.2
FOGAN 20 0.85 0.46 5.3
FOGAN 50 0.74 0.42 4.8
FOGAN 100 0.70 0.37 4.3
FOGAN 200 0.67 0.35 4.0
NSGGAN 20 0.59 0.28 3.6
NSGGAN 50 0.58 0.27 3.5
NSGGAN 100 0.58 0.24 3.2
NSGGAN 200 0.55 0.23 3.0
表 5  各模型在带钢表面缺陷数据集上的生成质量
方法 s p1-NN MMD WD
DCGAN 20 0.88 0.53 5.4
DCGAN 50 0.87 0.51 5.2
DCGAN 100 0.85 0.50 5.5
DCGAN 200 0.83 0.47 4.8
WGAN-GP 20 0.85 0.51 4.9
WGAN-GP 50 0.83 0.47 4.6
WGAN-GP 100 0.82 0.45 4.5
WGAN-GP 200 0.80 0.44 4.3
FOGAN 20 0.90 0.56 5.8
FOGAN 50 0.86 0.50 5.5
FOGAN 100 0.83 0.46 5.0
FOGAN 200 0.78 0.43 4.5
NSGGAN 20 0.75 0.46 4.4
NSGGAN 50 0.73 0.43 4.2
NSGGAN 100 0.70 0.42 4.0
NSGGAN 200 0.68 0.40 3.9
表 6  各模型在DAGM2007数据集上的生成质量
图 8  不同数量正样本下不同方法的F测度曲线
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