Chinese Journal of Liquid Crystals and Displays, Volume. 39, Issue 2, 237(2024)

Defect detection algorithm of improved YOLOv5s solar cell

Xueling PENG1,2, Shanling LIN1,2、*, Zhixian LIN1,2, and Tailiang GUO2
Author Affiliations
  • 1School of Advanced Manufacturing,Fuzhou University,Quanzhou 362252,China
  • 2Fujian Science and Technology Innovation Laboratory for Photoelectric Information,Fuzhou 350116,China
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    Aiming at the problem of low accuracy of the method for solar cell defect detection, a surface defect detection algorithm based on the improved YOLOv5s solar cell is proposed. First, in order to solve the problem of small target defect detection on the cell sheet, the Contextual Transformer Network (CoT) is proposed, which can provide global contextual information for small targets and the model better at predicting small targets. Secondly, by adding CBAM attention to the C3 module in the Head part, the important channels and spatial locations of the input feature maps can be better captured to improve the performance and robustness of the model. Next, the integrity of feature information is ensured by using CARAFE, a lightweight generalized up-sampling operator, to reduce the loss of feature information during up-sampling. Finally, by using WIoU as the bounding box loss function, the accuracy of the regression can be greatly improved and the convergence of model can be achieved quickly. The experimental results show that compared with the original algorithm, the improved YOLOv5s improves the three indicators of Precision, Recall, and mAP@0.5 by 5.5%, 4.1%, and 3.3% respectively, and the detection speed reaches 76 FPS, which meets the requirements of solar cell defect detection.

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    Xueling PENG, Shanling LIN, Zhixian LIN, Tailiang GUO. Defect detection algorithm of improved YOLOv5s solar cell[J]. Chinese Journal of Liquid Crystals and Displays, 2024, 39(2): 237

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    Paper Information

    Category: Research Articles

    Received: Aug. 5, 2023

    Accepted: --

    Published Online: Apr. 24, 2024

    The Author Email: Shanling LIN (sllin@fzu.edu.cn)

    DOI:10.37188/CJLCD.2023-0249

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