DEFECT DETECTION METHOD OF SOLAR CELLS BASED ON TRANSFER LEARNING AND RESNET

Jiang Shangjun, Yi Hui, Li Hongtao, Zeng Deshan

Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (7) : 116-121.

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (7) : 116-121. DOI: 10.19912/j.0254-0096.tynxb.2021-0555

DEFECT DETECTION METHOD OF SOLAR CELLS BASED ON TRANSFER LEARNING AND RESNET

  • Jiang Shangjun1, Yi Hui1, Li Hongtao2, Zeng Deshan3
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Abstract

In view of the complex and diverse types of solar cell defects and low manual detection efficiency, this paper proposes a solar cell defect detection method based on transfer learning and ResNet. This method first builds the ResNet defect detection model with the residual module, then uses the ImageNet pre-training model to initialize the ResNet model, and uses the EL image to fine-tune the defect detection model. Finally, the optimal model is loaded to build a classifier to realize the defect detection of solar cells. The proposed method uses transfer learning to optimize the training process, which can realize rapid model training and improve the accuracy of defect detection in the condition of small samples. To verify the effectiveness of the proposed method, both the proposed approach and some conventional approaches were applied to the real dataset collected from a solar cell production line in Changzhou. The comparative experiments show that the proposed method has the best comprehensive performance, strong generalization ability, and robustness. The proposed method can effectively realize the defect detection of solar cells and improves the production efficiency and quality of solar cells.

Key words

transfer learning / deep learning / solar cells / defect detection / EL images

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Jiang Shangjun, Yi Hui, Li Hongtao, Zeng Deshan. DEFECT DETECTION METHOD OF SOLAR CELLS BASED ON TRANSFER LEARNING AND RESNET[J]. Acta Energiae Solaris Sinica. 2023, 44(7): 116-121 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0555

References

[1] SAMPAIO P G V, GONZÁLEZ M O A. Photovoltaic solar energy: conceptual framework[J]. Renewable and sustainable energy reviews, 2017, 74: 590-601.
[2] 杨博, 赵勇, 谢小军, 等. PERC电池EL黑斑对于光伏组件性能的影响[J]. 太阳能学报, 2020, 41(12): 97-102.
YANG B, ZHAO Y, XIE X J, et al.Effect of EL dark spot on PERC photovoltaic module performance[J]. Acta energiae solaris sinica, 2020, 41(12): 97-102.
[3] 王宪保, 李洁, 姚明海, 等. 基于深度学习的太阳能电池片表面缺陷检测方法[J]. 模式识别与人工智能, 2014, 27(6): 517-523.
WANG X B, L I J, YAN M H, et al. Solar cells surface defects detection based on deep learning[J]. Pattern recognition and artificial intelligence, 2014, 27(6): 517-523.
[4] SUN M, LYU S, ZHAO X, et al.Defect detection of photovoltaic modules based on convolutional neural network[C]//Machine Learning and Intelligent Communications: Second International Conference(MLICOM), Weihai, China, 2018.
[5] 周颖, 毛立, 张燕, 等. 改进CNN的太阳电池缺陷识别方法研究[J]. 太阳能学报, 2020, 41(12): 69-76.
ZHOU Y, MAO L, ZHANG Y, et al.Research on defect detection and classification for solar cells based on improved convolutional neural network[J]. Acta energiae solaris sinica, 2020, 41(12): 69-76.
[6] AKEAM M W, LI G, JIN Y, et al.CNN based automatic detection of photovoltaic cell defects in electroluminescence images[J]. Energy, 2019, 189: 116319.
[7] TANG W Q, YANG Q, XIONG K X, et al.Deep learning based automatic defect identification of photovoltaic module using electroluminescence images[J]. Solar energy, 2020, 201: 453-460.
[8] 龙明盛. 迁移学习问题与方法研究[D]. 北京: 清华大学, 2014.
LONG M S.Transfer learning problems and methods[D]. Beijing: Tsinghua University, 2014.
[9] WANG M, DENG W.Deep visual domain adaptation: a survey[J]. Neurocomputing, 2018, 312: 135-153.
[10] PAN S J, YANG Q.A survey on transfer learning[J]. IEEE transactions on knowledge and data engineering, 2010, 22(10): 1345-1359.
[11] MADADI Y, SEYDI V, NASROLLAHI K, et al.Deep visual unsupervised domain adaptation for classification tasks: a survey[J]. IET image processing, 2020, 14(14): 3283-3299.
[12] 罗维平, 徐洋, 陈永恒, 等. 基于迁移学习和改进ResNet50网络的织物疵点检测算法[J]. 毛纺科技, 2021, 49(2): 71-78.
LUO W P, XU Y, CHEN Y H, et al.Fabric defect detection algorithm based on migration learning and improved of ResNet50 network[J]. Wool textile journal, 2021, 49(2): 71-78.
[13] HE K M, ZHANG X Y, REN S Q, et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Las Vegas, NV, USA, 2016.
[14] GLOROT X, BENGIO Y.Understanding the difficulty of training deep feedforward neural networks[C]//Proceedings of the thirteenth International Conference on Artificial Intelligence and Statistics(AISTATS), Sardinia, Italy, 2010.
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