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Investigation of the Training Data Set Influence on the Accuracy of the Optical Laguerre-Gaussian Modes Recognition

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Abstract

This paper investigates the accuracy of convolutional neural network recognition of Laguerre and Hermite-Gauss optical modes with geometric distortions in the form of affine transformations. The influence of training data sampling on the accuracy is analyzed. The ability of a convolutional neural network to recognize Laguerre and Hermite-Gaussian optical modes with geometric distortions caused by environmental distortions and described by affine transformations was also examined.

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  1. Samara National Research University, 443086, Samara, Russia

    A. V. Bekhterev

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Bekhterev, A.V. Investigation of the Training Data Set Influence on the Accuracy of the Optical Laguerre-Gaussian Modes Recognition. Opt. Mem. Neural Networks 32 (Suppl 1), S54–S62 (2023). https://doi.org/10.3103/S1060992X2305003X

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