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Estimating and monitoring laser induced damage size on glass windows with a deep-learning based pipeline

Abstract : Laser induced damage is a major issue in high power laser facilities such as Laser MegaJoule (LMJ) and National Ignition Facility (NIF) since they lower the efficiency of optical components and may even require their replacement. This problem occurs mainly in the final stages of the laser beamlines and in particular in the glass windows through which laser beams enter the central vacuum chamber. Monitoring such damage sites in high energy laser facilities is therefore of major importance. However, the automatic monitoring of damage sites is challenging due to the small size of damage sites and to the low resolution images provided by the onsite camera used to monitor their occurrence. A systematic approach based on a deep-learning computer vision pipeline is introduced to estimate the dimensions of damage sites of the glass windows of the LMJ facility. The ability of the pipeline to specialize in the estimation of damage sites of size less than the repair threshold is demonstrated by showing its higher efficiency than classical machine learning approaches in the specific case of damage site images. In addition, its performances on three datasets are evaluated to show both it robustness and accuracy.
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https://hal-cnrs.archives-ouvertes.fr/hal-03813714
Contributor : Nicolas Bonod Connect in order to contact the contributor
Submitted on : Thursday, October 13, 2022 - 3:01:07 PM
Last modification on : Tuesday, October 18, 2022 - 3:26:10 AM

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Isam Ben Soltane, Guillaume Hallo, Chloé Lacombe, Laurent Lamaignère, Nicolas Bonod, et al.. Estimating and monitoring laser induced damage size on glass windows with a deep-learning based pipeline. Journal of the Optical Society of America A (JOSA A), 2022, 39 (10), ⟨10.1364/JOSAA.462367⟩. ⟨hal-03813714⟩

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