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This work proposes a pattern recognition method for the superposition state orbital angular momentum (OAM) of vortex beams based on convolutional neural network (CNN) and improved vision transformer (VIT). Organically integrating the local feature extraction advantages of CNN with the global fast classification ability of VIT driven by sparse attention mechanism, using three sets of Laguerre-Gaussian (LG) beam patterns with superimposed light field intensity distribution maps of ocean turbulence distortion as input, efficient and accurate recognition of end-to-end wavefront distortion is realized. MATLAB numerical simulation is adopted to simulate the superposition state LG beam in ocean turbulent environment, power spectrum inversion method is used to simulate ocean turbulence, and recognition accuracy and confusion matrix are used as evaluation indicators for OAM pattern recognition. The experimental results show that the CNN-VIT model exhibits excellent performance in OAM pattern recognition accuracy under different ocean turbulence intensities, wavelengths, transmission distances, and mode intervals. Compared with existing CNN and VIT, the proposed model improves recognition accuracy by 23.5% and 9.65% respectively under strong ocean turbulence conditions, thus exhibiting strong generalization ability under unknown ocean turbulence strengths. This demonstrates the potential application of the CNN-VIT model in OAM pattern recognition of vortex light superposition states.
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Keywords:
- vortex beam /
- ocean turbulence /
- convolutional neural network /
- mode recognition
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图 1 3组OAM模式集中16种叠加LG光束强度分布图像 (a) Set1, Δl = 1; (b) Set2, Δl = 3; (c) Set3, Δl = 5
Figure 1. The 16 superimposed LG beam intensity images in three OAM mode sets: (a) Set1, Δl = 1; (b) Set2, Δl = 3; (c) Set3, Δl = 5.
图 2 功率谱反演法模拟海洋湍流光强扰动
Figure 2. Simulation of oceanic turbulence using power spectrum inversion method.
图 3 CNN 和VIT 融合网络结构
Figure 3. Fusion network structure of CNN and VIT.
图 4 不同湍流情况下识别准确率随迭代次数的变化
Figure 4. Variation of recognition accuracy with the number of iterations under different turbulence conditions.
图 5 不同距离(a)和不同波长(b)下识别准确率随迭代次数的变化
Figure 5. Variation of recognition accuracy with iteration count at different distances (a) and different wavelengths (b).
图 6 不同模型识别准确率随传输距离(a)和湍流强度(b)的变化
Figure 6. Variation of recognition accuracy of different models with transmission distance (a) and turbulence intensity (b).
图 7 不同模型混淆矩阵
Figure 7. Confusion matrix of different models.
图 8 不同湍流强度下识别准确率随不同模式间隔的变化
Figure 8. Variation of recognition accuracy with different mode intervals under different turbulence intensities.
表 1 LG光束模式选择
Table 1. LG beam mode selection.
组别 拓扑荷数间隔 叠加OAM模式 Set1 $ \Delta l=1 $ $ \left\{{p}_{2}=0, 1, 2, 3;\right.\left.{l}_{2}=-2, -1, 0, 1\right\} $ Set2 $ \Delta l=3 $ $ \left\{{p}_{2}=0, 1, 2, 3;\right.\left.{l}_{2}=-5, -2, 1, 4\right\} $ Set3 $ \Delta l=5 $ $ \left\{{p}_{2}=0, 1, 2, 3;\right.\left.{l}_{2}=-8, -3, 2, 7\right\} $ 
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表 2 硬件平台配置和模型参数
Table 2. Hardware platform configuration and model parameters.
配置 型号 参数设置 数值 操作系统 Windows10 迭代次数 100 CPU Intel40核E5
2670处理器批次值 16 GPU NVIDIA
RTX3090学习率 0.01 内存 64 G 标签平滑 Le-5 CDUA 11 学习率
衰减模型Cosine Annealing
LrUpdater编程语言 Python3.8 优化器类型 Adam
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