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 root mean 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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References (11)
02Google Colaboratory (Springer) pp 59–64 URL https: //link.springer.com/book/10.1007/978-1-4842-4470-82019
03A 2016 Lasers in Surgery and Medicine2016
04Iso 11146-1: Lasers and laser-related equipment-test methods for laser beam widths, divergence angles and beam propagation ratios-part 1: Stigmatic and simple astigmatic beams2005
05Laser beam quality metrics (SPIE Press) URL https: //doi.org/10.1117/3.10005952013
06DPSS (Diode Pumped Solid State) Lasers: Applications and Issues MQ1 URL1998
07Computer Vision -ECCV 818-833 URL2014
08Frontiers in Neural Circuits 2 ISSN 1662-5110 URL https: //doi.org/10.3389/neuro.04.005.20082008
09Joint European Conference on Machine Learning and Knowledge Discovery in Databases2013
10Computer Vision – ECCV 740–755 URL https: //doi.org/10.1007/978-3-319-10602-12014
11A 2018 arXiv preprint arXiv:1804.02767 URL https://arxiv.org/abs/1804.027672018