Abstract
Laser-induced damage is a major issue in high power laser facilities
such as the Laser MégaJoule (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 a 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
robustness and accuracy.
Subject
Computer Vision and Pattern Recognition,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials
Cited by
1 articles.
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