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Measuring Laser Beams with a Neural Network
Optics · Submitted 16 Feb 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | A deep neural network (NN) is used to simultaneously detect laser beams in images and measure their center coordinates, radii and angular orientations. | contribution 0.61 | 0.87 | 0.15 | asserted 1.00 |
| s1 | 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. | method 1.00 | 0.88 | 0.03 | asserted 0.98 |
| s2 | After training on the simulated dataset the NN achieves beam parameter rootmean-square-errors (RMSEs) of less than 3.4% on the experimental dataset. | result 1.00 | 0.94 | 0.21 | asserted 1.00 |
| s3 | Subsequent training on the experimental dataset causes the RMSEs to fall below 1.1%. | result 1.00 | 0.94 | 0.19 | asserted 1.00 |
| s4 | 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. | interpretation 0.91 | 0.48 | 0.40 | hedged possibility 0.70 |
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The paper
Original 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.
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What it cites · 1 sentence without a confident label
Uses the method or tool of 3 papers
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The RROIs and feature map are both passed into the RROI alignment stage [ref] which returns fixed size feature maps via bi-linear interpolation as successive layers require a fixed input.
Improving Rotated Text Detection with Rotation Region Proposal Networks · Rotated region proposal neural network · 0.98
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Bayesian optimization (BO) [ref] is used to tune the hyperparameters within sensible bounds (see Table [ref])—this time with a single learning rate decay—using Facebook's Ax/BoTorch [ref] package.
A Tutorial on Bayesian Optimization · Training and evaluation · 1.00
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Bayesian optimization (BO) [ref] is used to tune the hyperparameters within sensible bounds (see Table [ref])—this time with a single learning rate decay—using Facebook's Ax/BoTorch [ref] package.
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization · Training and evaluation · 1.00
Cites as background 1 paper
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Object detection neural networks (ODNN) [ref] , which are based on CNNs, can detect objects in images, classify the objects [ref] and determine regions-of-interest (ROIs) which bound the objects.
YOLOv3: An Incremental Improvement · Introduction · 0.89
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