Measuring Laser Beams with a Neural Network

TL;DR

This paper presents a neural network approach for accurately detecting and measuring laser beam parameters in images, using simulated and experimental datasets to achieve high precision in beam characterization.

Contribution

The study introduces a neural network that simultaneously detects laser beams and measures their parameters, trained on both simulated and experimental data for improved accuracy.

Findings

RMSEs less than 3.4% on experimental data after simulation training

RMSEs below 1.1% after training on experimental data

Neural network can serve as a standalone or complementary beam profiling tool

Abstract

A deep neural network (NN) is used to simultaneously detect laser beams in images and measure their center coordinates, radii and angular orientations. A dataset of images containing simulated laser beams and a dataset of images with experimental laser beams, generated using a spatial light modulator, are used to train and evaluate the NN. After training on the simulated dataset the NN achieves beam parameter rootmean-square-errors (RMSEs) of less than 3.4% on the experimental dataset. Subsequent training on the experimental dataset causes the RMSEs to fall below 1.1%. The NN method can be used as a stand-alone measurement of the beam parameters or can compliment other beam profiling methods by providing an accurate region-of-interest.