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本文提出了一种基于卷积神经网络(Convolutional Neural Network,CNN)与改进Vision Transformer（ VIT）的涡旋光束叠加态轨道角动量(Orbital AngularMomentum,OAM)模式识别方法。以海洋湍流畸变的三组拉盖尔-高斯光束模式叠加光场强度分布图为输入，有机整合了CNN的局部特征提取优势与稀疏注意力机制驱动的VIT的全局快速分类能力，实现端到端的波前畸变高效精准识别。通过数值仿真模拟海洋湍流环境叠加态OAM模式，利用功率谱反演法模拟海洋湍流，以识别准确率和混淆矩阵作为OAM模式识别的评估指标。实验结果表明，CNN-VIT模型在不同海洋湍流强度、波长、传输距离和模式间隔条件下均展现出优异的OAM模式识别准确率性能。与现有的CNN和VIT相比，本文模型在强海洋湍流条件下识别准确率分别提升了23.5%和9.65%。这证明了CNN-VIT模型在涡旋光叠加态OAM模式识别的应用潜力。This article 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 LG beam patterns with superimposed light field intensity distribution maps of ocean turbulence distortion as input, achieving efficient and accurate recognition of end-to-end wavefront distortion. Using MATLAB numerical simulation 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 intensity, wavelength, transmission distance, and mode interval. Compared with existing CNN and VIT, the proposed model has improved recognition accuracy by 23.5% and 9.65% respectively under strong ocean turbulence conditions,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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[1] Erhard M, Fickler R, Krenn M 2018 Light: Science & Applications. 7 17146
[2] Tamburini F, Anzolin G, Umbriaco G 2006 Physical Review Letters. 97 163903
[3] Shen Y J, Wang X J, Xie Z W 2019 Light: Science & Applications. 8 90
[4] Molina Terriza G, Torres J P, Torner L 2001 Physical Review Letters. 88 013601
[5] Wang J, Yang J Y, Fazal I M 2012 Nature photonics. 6 488
[6] Ren Y, Huang H, Xie G 2013 Optics Letters. 38 4062
[7] Zhu X L, Guo L, Zhu Q 2018 IEEE Photonics Journal. 10 135
[8] Fan W Q, Gao F L, Xue F C, Guo J J, Xiao Y, Gu Y J 2024 Appl. Opt. 63 982
[9] Krizhevsky A, Sutskever I, Hinton G E 2017 Communications of the ACM. 60 84
[10] Yang C Y, Shan K G 2020 Journal of South-Central University for Nationalities. 39 390
[11] Wang X Y, Wang Z Y, Cheng Z Y 2022 Chinese Journal Of Quantum Electronic. 39 956
[12] Wang Z L, Li X F, Cai Y J, Liu X L 2025 Optics Express. 33 6
[13] Guo Y, Lv H, Ding C L, Yuan C Z, Jin R B 2025 Acta Phys. Sin. 74 1（in Chinese） [郭焱, 吕恒, 丁春玲, 袁晨智, 金锐博 2025 物理学报 74 1]
[14] Zhang C Z, Cao Y, Tu Q L, Peng X F 2023 Laser Journal. 22 062235（in Chinese） [张成志，曹阳，涂巧玲，彭小峰 2023 激光杂志22 062235]
[15] Zhou X, Chen C Y, Yu H Y 2023 Laser & Optoelectronics Progress. 60 2306003
[16] Wang J, Wang C B, Tan Z K, Lei S C, Wu P F 2024 Scientia Sinica Physica, Mechanica & Astronomica. 54 284211
[17] Lu J N, Cao C Y, Zhu Z Q 2020 Applied Physics Letters. 116 201105
[18] Wu P F, Wang X D, Wang J 2023 Acta Optica. 43 257（in Chinese） [吴鹏飞, 王小蝶, 王姣 2023光学学报 43 257]
[19] Cai D M, Wang K, Jia P 2014 Acta Phys. Sin. 63 104217（in Chinese） [蔡德明, 王科, 贾鹏 2014 物理学报 63 104217]
[20] Nikishov V 2000 International Journal of Fluid Mechanics Research. 27 82
[21] Hu J, Shen L, Albanie S, Sun G 2020 IEEE Trans. Pattern Anal. Mach. Intell. 42 2011
[22] Wang T, Lan J, Han Z 2022 Frontiers in Neuroscience. 16 5
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