Abstract
A number of vision-based methods for detecting laser-induced defects on optical components have been implemented to replace the time-consuming manual inspection. While deep-learning-based methods have achieved state-of-the-art performances in many visual recognition tasks, their success often hinges on the availability of a large number of labeled training sets. In this paper, we propose a surface defect detection method based on image segmentation with a U-shaped convolutional network (U-Net). The designed network was trained on paired sets of online and offline images of optics from a large laser facility. We show in our experimental evaluation that our approach can accurately locate laser-induced defects on the optics in real time. The main advantage of the proposed method is that the network can be trained end to end on small samples, without the requirement for manual labeling or manual feature extraction. The approach can be applied to the daily inspection and maintenance of optical components in large laser facilities.
Highlights
Defects on the surface of optics are among the earliest indications of degradation which are critical for the maintenance of optical systems
We present our approach to detection of optical defects that leverages the power of U-shaped convolutional network (U-Net)
A vision-based approach for detecting optical defects has been proposed based on image segmentation
Summary
Defects on the surface of optics are among the earliest indications of degradation which are critical for the maintenance of optical systems. Detection of the defects allows preventive measures to be taken to prevent the defects from growing to an unrepairable size. Large laser facilities, such as the National Ignition Facility (NIF)[1] and the Laser Megajoule (LMJ)[2], routinely operate at high ultraviolet fluences above the damage threshold of optical components. Various image processing techniques, such as the threshold method, Otsu’s method and Fourier transform[3,4,5], have been implemented for defect detection to replace the timeconsuming and error-prone manual inspection. Using linescan phase-differential imaging, LLNL developed a process for rapid detection of phase defects in the bulk or surface of large-aperture
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