OAM mode recognition system of vortex beams based on deep learning and FPGA

ZHANG Chen; LV Hong; WANG Kunpeng; LIU Yike; WU Tongqiao; SHAO Ruikang; HUANG Dingjin

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OAM mode recognition system of vortex beams based on deep learning and FPGA

Optical Information Technology | 更新时间：2025-10-09

- OAM mode recognition system of vortex beams based on deep learning and FPGA
- OPTICAL TECHNIQUE Vol. 51, Issue 4, Pages: 392-399(2025)
- 作者机构：
  
  西安工业大学光电工程学院，陕西西安 710021
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN249
- Received：13 March 2025，
  
  Revised：2025-04-14，
  
  Published：20 July 2025
- 稿件说明：
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张尘,吕宏,王鲲鹏等.基于深度学习与FPGA的涡旋光束OAM模式识别[J].光学技术,2025,51(04):392-399.

ZHANG Chen,LV Hong,WANG Kunpeng,et al.OAM mode recognition system of vortex beams based on deep learning and FPGA[J].OPTICAL TECHNIQUE,2025,51(04):392-399.
张尘,吕宏,王鲲鹏等.基于深度学习与FPGA的涡旋光束OAM模式识别[J].光学技术,2025,51(04):392-399. DOI：

ZHANG Chen,LV Hong,WANG Kunpeng,et al.OAM mode recognition system of vortex beams based on deep learning and FPGA[J].OPTICAL TECHNIQUE,2025,51(04):392-399. DOI：

摘要

为提高结构化涡旋光束轨道角动量（OAM）的检测效率，提出结合深度学习与FPGA加速的OAM模式识别方法。利用功率谱反演法生成随机相位屏，通过空间光调制器模拟五种大气湍流环境，采集涡旋光束经过随机相位屏后的光斑数据图像，构建包含10000组样本的数据集，采用ResNet18卷积神经网络（CNN），对比分析不同湍流强度对涡旋光束OAM模式识别精度的影响，并对模型的识别性能进行全面评估，最终将训练后的ResNet18神经卷积网络模型载入可编程门阵列（FPGA）硬件平台实现OAM模式识别。结果表明：在弱、中湍流环境下的三种OAM模式识别准确率达100%，强湍流环境下的两种OAM模式识别准确率仍保持99%，相较于弱、中湍流环境下的OAM模式识别准确率略有下降。通过将CNN模型载入FPGA硬件平台，实现了耗能2.844/W、能效比2.19GOPS/W、推理时间0.58s的涡旋光束OAM模式识别。

Abstract

To enhance the detection efficiency of structured vortex beams carrying Orbital Angular Momentum （OAM）

a recognition method combining deep learning with FPGA acceleration is proposed. Random phase screens are generated using the power spectrum inversion method

and five types of atmospheric turbulence conditions are simulated via a spatial light modulator. Vortex beam spot images after passing through the random phase screens are collected to construct a dataset comprising 10

000 samples. A ResNet18 Convolutional Neural Network （CNN） is employed to analyze and compare the impact of different turbulence intensities on OAM mode recognition accuracy. The recognition performance of the model is comprehensively evaluated

and the trained ResNet18 model is subsequently deployed onto a Field-Programmable Gate Array （FPGA） hardware platform for real-time OAM mode recognition. Experimental results demonstrate that under weak and moderate turbulence conditions

the recognition accuracy for three OAM modes reaches 100%

while under strong turbulence

the accuracy for two OAM modes remains as high as 99%

showing a slight decrease compared to the weaker turbulence cases. By deploying the CNN model onto the FPGA platform

the system achieves an energy consumption of 2.844 W

an energy efficiency of 2.19 GOPS/W

and an inference time of 0.58 seconds for OAM mode recognition of vortex beams.

关键词

Keywords

references

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