Orbital Angular Momentum Multi-Superposition Identification Method Based on Attention Mechanism

Xu Zhou; Chunyi Chen; Haiyang Yu; Xiaolong Ni; Xiaojuan Hu

doi:10.3788/LOP223103

Laser & Optoelectronics Progress, Volume. 60, Issue 23, 2306003(2023)

Orbital Angular Momentum Multi-Superposition Identification Method Based on Attention Mechanism

Xu Zhou^1,2, Chunyi Chen^1,2、*, Haiyang Yu^1,2, Xiaolong Ni¹, and Xiaojuan Hu^1,2

¹Key Laboratory of Photoelectric Measurement & Control and Optical Information Transfer Technology, Ministry of Education, Changchun University of Science and Technology, Changchun 130022, Jilin, China

²School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, Jilin, China

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Figures & Tables(9)

Fig. 1. OAM superimposed beams. (a)‒(c) Different waist radius; (d)‒(f) radial index is not 0

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Fig. 2. Schematic diagram of SENet block

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Fig. 3. Schematic diagram of SE-VGG-16 model

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Fig. 4. Identification of superimposed OAM modal diagram

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Fig. 5. OAM superimposed beams affected by different atmospheric turbulence intensities

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Fig. 6. Accuracy curve of OAM superimposed beam modes obtained by trained model with different combinations of girdle radius

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Fig. 7. Recognition accuracy curves of OAM superimposed beam obtained by improved model. (a) Different radial indexes; (b) different wavelengths; (c) different combinations of girdle radius

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Table 1. Recognition accuracy of different backbone networks on mixed dataset
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Table 1. Recognition accuracy of different backbone networks on mixed dataset
Backbone Number of layers Accuracy /%
LeNet 5 79.64
AlexNet 8 79.82
VGG-16 16 85.36
InceptionNet-V1 22 79.46
ResNet 50 78.21
DenseNet 121 76.61

Table 2. Ablation experiments of improved network models on mixed dataset
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Table 2. Ablation experiments of improved network models on mixed dataset
VGG-16 BN+Dropout SENet block Accuracy /%
√ 85.36
√ √ 87.67
√ √ 86.96
√ √ √ 89.82

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Xu Zhou, Chunyi Chen, Haiyang Yu, Xiaolong Ni, Xiaojuan Hu. Orbital Angular Momentum Multi-Superposition Identification Method Based on Attention Mechanism[J]. Laser & Optoelectronics Progress, 2023, 60(23): 2306003

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