Open AccessDevelopment of a defect recognition algorithm for visual laser-induced damage detection
Author(s) -
Tamás Somoskői,
Csaba Vass,
Péter Jójárt,
. P Santha,
K. Osvay
Publication year - 2020
Publication title -
laser physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.377
H-Index - 55
eISSN - 1555-6611
pISSN - 1054-660X
DOI - 10.1088/1555-6611/ab7a24
Subject(s) - computer science , sensitivity (control systems) , visual inspection , noise (video) , reduction (mathematics) , artificial intelligence , reliability (semiconductor) , algorithm , computer vision , laser , noise reduction , image processing , human visual system model , image (mathematics) , pattern recognition (psychology) , optics , mathematics , physics , electronic engineering , power (physics) , geometry , quantum mechanics , engineering
Laser-induced damage is defined as a permanent detrimental change in the characteristics of an optical element caused by a laser beam. This change can be observed by many different inspection techniques, of which optical and phase imaging microscopic techniques have superior sensitivity. However, such examinations conducted by human operators are relatively slow and subjective—so they cannot be used for online damage monitoring purposes, whereas automatic inspection systems have advantages in terms of sensitivity, reliability, and speed. In this paper we introduce a new method for the computer-aided recognition of damaged sites based on visual images taken from the sample surface by a CCD camera. The evaluation procedure is performed by a computer algorithm, which consists of exact, statistically established steps. It includes noise reduction by considering the statistical behavior of photon noise. Besides, it takes into account the spatial extent of a damage spot by nonlinear image filtering to separate damage-indicating intensity changes from random noise. This mimics the ability of the human eye to distinguish features from their surroundings. The evaluation algorithm is built of computationally less demanding mathematical operations to enable fast execution which is vital for monitoring at high repetition rates. The proposed method was tested on a sizeable dataset of images yielding 98.8% of damage detection efficiency. It was also compared to a generally used visual laser damage detection procedure, which has a success rate of 88.6%. This yields one order of magnitude reduction in the number of undetected damaged sites.