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Laser & Optoelectronics Progress, Volume. 62, Issue 14, 1406001(2025)

Superposition Vortex Beam Recognition Using Convolutional Neural Network and Swin Transformer

Jun Li1, Zhongrui Tao1, Jin Liu2, Fuxing Xu1, Chengliang Gou1, Xiaowei Xu1, Dawei Zhang1, and Haima Yang1,3、*
Author Affiliations
  • 1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
  • 2College of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
  • 3Key Laboratory of Space Active Opto-Electronics Technology, Chinese Academy of Sciences, Shanghai 201800, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (9)
    Equations (8)
    References (23)
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    Figures & Tables(9)
    Intensity distributions of 16 multi-mode vortex beams

    Fig. 1. Intensity distributions of 16 multi-mode vortex beams

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    Model system for superimposed beam transport in atmospheric turbulence

    Fig. 2. Model system for superimposed beam transport in atmospheric turbulence

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    Network structure diagram based on CNN and SWT fusion model

    Fig. 3. Network structure diagram based on CNN and SWT fusion model

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    Schematic diagrams of the calculation method of the multi-head self-attention mechanism of the moving window

    Fig. 4. Schematic diagrams of the calculation method of the multi-head self-attention mechanism of the moving window

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    Superimposed vortex light intensity maps

    Fig. 5. Superimposed vortex light intensity maps

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    Recognition accuracy under different atmospheric turbulence based on CNN-SWT combined neural network. (a) Moderate turbulence; (b) moderate-to-strong turbulence; (c) strong turbulence; (d) comparison of recognition accuracy between three types of turbulence

    Fig. 6. Recognition accuracy under different atmospheric turbulence based on CNN-SWT combined neural network. (a) Moderate turbulence; (b) moderate-to-strong turbulence; (c) strong turbulence; (d) comparison of recognition accuracy between three types of turbulence

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    Accuracy of different networks under different conditions. (a) Detection accuracy under moderate-to-strong turbulence at different transmission distances; (b) detection accuracy under different turbulence intensities at z = 1500 m

    Fig. 7. Accuracy of different networks under different conditions. (a) Detection accuracy under moderate-to-strong turbulence at different transmission distances; (b) detection accuracy under different turbulence intensities at z = 1500 m

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    Comparison of confusion matrices

    Fig. 8. Comparison of confusion matrices

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    • Table 1. CNN structure table

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      Table 1. CNN structure table

      Input layerOut dimensionNumber of parameters
      Conv2D_1224×224×16160
      Conv2D_2224×224×162320
      MaxPooling1112×112×160
      Conv2D_3112×112×324640
      Conv2D_4112×112×329248
      MaxPooling256×56×320
      Conv2D_556×56×6418496
      Conv2D_656×56×6436928
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    Jun Li, Zhongrui Tao, Jin Liu, Fuxing Xu, Chengliang Gou, Xiaowei Xu, Dawei Zhang, Haima Yang. Superposition Vortex Beam Recognition Using Convolutional Neural Network and Swin Transformer[J]. Laser & Optoelectronics Progress, 2025, 62(14): 1406001

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    Paper Information

    Category: Fiber Optics and Optical Communications

    Received: Dec. 28, 2024

    Accepted: Feb. 19, 2025

    Published Online: Jul. 16, 2025

    The Author Email: Haima Yang (snowyhm@sina.com)

    DOI:10.3788/LOP242520

    CSTR:32186.14.LOP242520

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology