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2025 , Vol. 30 >Issue 5: 1050 - 1064

DOI: https://doi.org/10.1007/s12204-023-2670-3

Computing & Computer Technologies

Novel Multi-Step Deep Learning Approach for Detection of Complex Defects in Solar Cells

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  • College of Mechanical Engineering, Donghua University, Shanghai 201620, China

Received date: 2023-04-11

  Accepted date: 2023-06-20

  Online published: 2023-11-06

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Abstract

Solar cell defects exhibit significant variations and multiple types, with some defect data being difficult to acquire or having small scales, posing challenges in terms of small sample and small target in defect detection for solar cells. In order to address this issue, this paper proposes a multi-step approach for detecting the complex defects of solar cells. First, individual cell plates are extracted from electroluminescence images for block-by-block detection. Then, StyleGAN2-Ada is utilized for generative adversarial networks data augmentation to expand the number of defect samples in small sample defects. Finally, the fake dataset is combined with real dataset, and the improved YOLOv5 model is trained on this mixed dataset. Experimental results demonstrate that the proposed method achieves a superior performance in detecting the defects with small sample and small target, with the final recall rate reaching 99.7%, an increase of 3.9% compared with the unimproved model. Additionally, the precision and mean average precision are increased by 3.4% and 3.5%, respectively. Moreover, the experiments demonstrate that the improved network training on the mixed dataset can effectively enhance the detection performance of the model. The combination of these approaches significantly improves the network’s ability to detect solar cell defects.

Cite this article

JIANG Wenbo, ZHENG Hangbin, BAO Jinsong . Novel Multi-Step Deep Learning Approach for Detection of Complex Defects in Solar Cells[J]. Journal of Shanghai Jiaotong University(Science), 2025 , 30(5) : 1050 -1064 . DOI: 10.1007/s12204-023-2670-3

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