Research on defect detection algorithm of monocrystalline silicon cell based on domain adaptation

Qinghua Liu; Zhenying Xu; Yucheng Tao; Linhang Li; Yuxuan Zhang

doi:10.1117/12.2617298

19 November 2021 Research on defect detection algorithm of monocrystalline silicon cell based on domain adaptation

Qinghua Liu, Zhenying Xu, Yucheng Tao, Linhang Li, Yuxuan Zhang

Proceedings Volume 12059, Tenth International Symposium on Precision Mechanical Measurements; 1205920 (2021) https://doi.org/10.1117/12.2617298
Event: Tenth International Symposium on Precision Mechanical Measurements, 2021, Qingdao, China

Abstract

The variousness as well as the inaccessibility of defect characteristics make the defect detection of monocrystalline silicon solar cells more challenging. To address these problems, a novel domain adaptive target detection algorithm based on pseudo-label learning, which is an efficient and feasible weakly supervised learning method, is proposed in this paper. Firstly, in the early stage of the model, the loss function is improved to solve the problem that the model is not easy to converge due to a large number of false labels in the pseudo labels. Then, to classify the sample space better, entropy regularization is applied to the sample boundary data, and the unlabels of the target domain images are labeled with pseudo labels. Finally, the source domain data and the target domain data are used for training together, and the generalization performance of the model obtained is greatly improved. The results show that in solar cell image detection, the accuracy of the domain adaptive method based on pseudo-label learning can reach over 90%, which is better than the target detection accuracy of using only the source domain dataset.

Citation Download Citation

Qinghua Liu, Zhenying Xu, Yucheng Tao, Linhang Li, and Yuxuan Zhang "Research on defect detection algorithm of monocrystalline silicon cell based on domain adaptation", Proc. SPIE 12059, Tenth International Symposium on Precision Mechanical Measurements, 1205920 (19 November 2021); https://doi.org/10.1117/12.2617298

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