To identify different modes of orbital angular momentum (OAM) vortex beams after demultiplexing, deep learning technology is introduced, and a convolutional neural network (CNN) model is designed to detect OAM beams. The light intensity distribution maps of the Laguerre Gaussian beam with the topological charge from 1 to 20 through experiments are collected, the random phase screens are generated by using the power spectrum inversion method, to simulate the transmission of Laguerre Gaussian beams in the different atmospheric turbulence channels. The recognition accuracy is studied by the conditions of different CNN model iteration times, beam wavelengths, and data sets. The images are classified and processed to make different data sets, and the random vortex beam training sets are tested. Experimental results show that this method with about 98% accuracy for the conditions of long‐wavelength beams and medium or weak turbulence. The training on complex training sets can improve the effect.

中文翻译：

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基于卷积神经网络的轨道角动量涡旋光束识别

为了识别解复用后的轨道角动量（OAM）涡旋光束的不同模式，引入了深度学习技术，并设计了卷积神经网络（CNN）模型来检测OAM光束。通过实验收集了拓扑电荷为1到20的Laguerre高斯光束的光强分布图，使用功率谱反演方法生成了随机相位屏蔽，以模拟Laguerre高斯光束在不同大气湍流中的透射率渠道。通过不同的CNN模型迭代时间，光束波长和数据集的条件来研究识别精度。对图像进行分类和处理以形成不同的数据集，并对随机涡流训练集进行测试。实验结果表明，该方法在长波光束，中湍流或弱湍流条件下的准确度约为98％。对复杂的训练集进行训练可以提高效果。