Segmentation of small defects in Final Optics Damage Online Inspection images

Bao, Feng; Chen, Fengdong; Liu, Bingguo; Liu, Guodong

doi:10.1109/iasp.2012.6425003

Cited by 4 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To identify the damage sites in images, an algorithm based on the analysis of the Local Area Signal-to-Noise Ratio (LASNR) for each pixel of each image was proposed [21]. Another method based on local area 40 signal strength and 2D histogram was then proposed to detect damage sites [22]. To reconstruct the shape of damage sites precisely, a region growing algorithm is used [21].…”

Section: Introductionmentioning

confidence: 99%

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

SOLTANE¹,

Hallo

Lacombe

et al. 2022

J. Opt. Soc. Am. A

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

SOLTANE¹,

Hallo

Lacombe

et al. 2022

J. Opt. Soc. Am. A

View full text Add to dashboard Cite

show abstract

“…The signal image can be obtained by processing an FODI image using a Gaussian high-pass filter, whereas the noise image can be obtained by processing FODI images using a Gaussian low-pass filter. 10,11 According to the 12 feature parameters of the light intensity distribution on the CCD surface in the simulation formed by pit, Att 1 , and Att 2 , we present the 12 parameters that correspond to the former parameters for describing true and false damage in the experimental FODI images: (1) Table 3.…”

Section: True and Att-type False Damage In An Fodi Imagementioning

confidence: 99%

“…Each team has developed its own experimental version of the final optics damage inspection (FODI) system, namely, the NIF FODI developed for the United States National Ignition Facility (NIF) by LLNL scientists 8,9 and the SG-III FODI we developed for the SG-III laser facility at the CAEP. 10 In general, FODI is an online imaging system based on machine vision for rapidly observing laser-induced damage. With edge illumination technology in the dark field, FODI captures images of the damage on an FOA surface and obtains the damage information by image processing.…”

Section: Introductionmentioning

confidence: 99%

Automatic classification of true and false laser-induced damage in large aperture optics

et al. 2018

View full text Add to dashboard Cite

Abstract. An automatic classification method based on machine learning is proposed to distinguish between true and false laser-induced damage in large aperture optics. First, far-field light intensity distributions are calculated via numerical calculations based on both the finite-difference time-domain and the Fourier optical angle spectrum theory for Maxwell's equations. The feature vectors are presented to describe the possible damage sites, which include true and false damage sites. Finally, a kernel-based extreme learning machine is used for automatic recognition of the true sites and false sites. The method studied in this paper achieves good recognition of false damage, which includes a variety of types, especially attachment-type false damage, which has rarely been studied before. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract