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Volume 13
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10.3390/cryst13050819
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Article

A Definition Rule for Defect Classification and Grading of Solar Cells Photoluminescence Feature Images and Estimation of CNN-Based Automatic Defect Detection Method

by
Mingyu Gao
1,2,
Yunji Xie
1,2,
Peng Song
1,3,*,
Jiahong Qian
2,
Xiaogang Sun
3 and
Junyan Liu
1,2,*
1
State Key Laboratory of Robotics and System (HIT), Harbin Institute of Technology, Harbin 150001, China
2
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
3
School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
*
Authors to whom correspondence should be addressed.
Crystals 2023, 13(5), 819; https://doi.org/10.3390/cryst13050819
Submission received: 4 April 2023 / Revised: 27 April 2023 / Accepted: 5 May 2023 / Published: 15 May 2023
(This article belongs to the Section Materials for Energy Applications)
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Abstract

A nondestructive detection method that combines convolutional neural network (CNN) and photoluminescence (PL) imaging was proposed for the multi-classification and multi-grading of defects during the fabrication process of silicon solar cells. In this paper, the PL was applied to collect the images of the defects of solar cells, and an image pre-processing method was introduced for enhancing the features of the defect images. Simultaneously, the defects were defined by 13 categories and three divided grades of each under the definition rules of defects that were proposed in accordance with distribution and characteristics of each defect category, and expand data were processed by various data augmentation. The model was therefore improved and optimized based on the YOLOv5 as the feature extractor and classifier. The capability of the model on distinguishing categories and grades of solar cell defects was improved via parameter tuning and image pre-processing. Through experimental analysis, the optimal combination of hyperparameters and the actual effect of data sample pre-processing on the training results of the neural network were determined. Conclusively, the reasons for the poor recognition results of the small target defects and complex feature defects by the current model were found and further work was confirmed under the foundation of the differences in recognition results between different categories and grades.
Keywords: photoluminescence imaging; defects definition rule; solar cell; target detection; deep learning photoluminescence imaging; defects definition rule; solar cell; target detection; deep learning

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MDPI and ACS Style

Gao, M.; Xie, Y.; Song, P.; Qian, J.; Sun, X.; Liu, J. A Definition Rule for Defect Classification and Grading of Solar Cells Photoluminescence Feature Images and Estimation of CNN-Based Automatic Defect Detection Method. Crystals 2023, 13, 819. https://doi.org/10.3390/cryst13050819

AMA Style

Gao M, Xie Y, Song P, Qian J, Sun X, Liu J. A Definition Rule for Defect Classification and Grading of Solar Cells Photoluminescence Feature Images and Estimation of CNN-Based Automatic Defect Detection Method. Crystals. 2023; 13(5):819. https://doi.org/10.3390/cryst13050819

Chicago/Turabian Style

Gao, Mingyu, Yunji Xie, Peng Song, Jiahong Qian, Xiaogang Sun, and Junyan Liu. 2023. "A Definition Rule for Defect Classification and Grading of Solar Cells Photoluminescence Feature Images and Estimation of CNN-Based Automatic Defect Detection Method" Crystals 13, no. 5: 819. https://doi.org/10.3390/cryst13050819

APA Style

Gao, M., Xie, Y., Song, P., Qian, J., Sun, X., & Liu, J. (2023). A Definition Rule for Defect Classification and Grading of Solar Cells Photoluminescence Feature Images and Estimation of CNN-Based Automatic Defect Detection Method. Crystals, 13(5), 819. https://doi.org/10.3390/cryst13050819

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