You Li-Hua
School of Mechanical Engineering Jiangnan University, Wuxi, 214122, Jiangsu, China
Wu Jing-Jing
School of Mechanical Engineering Jiangnan University, Wuxi, 214122, Jiangsu, China
ABSTRACT
This study investigates the issue of automated defect inspection for aluminum alloy and proposes a new defect detection method based on a maneuver detector, i.e., the Generalized Likelihood Ratio (GLR) detector. In this method, the intensity of the defect-free aluminum alloy image is supposed to be Gaussian distributed, while the defect intensity usually follows other statistical distributions. In terms of this different statistic property between the normal and abnormal area in an aluminum alloy image, an unknown input is employed to model the change of intensity distribution. Under the assumptions, defect inspection problem is approximated as the detection of abrupt changes in stochastic dynamical system. Kalman filters are used to filter the image and the measurement residuals are estimated. Defects are located by statistical tests on measurement innovations using the Generalized Likelihood Ratio (GLR) test based maneuver detector. Experimental results exhibit effective defect detection for aluminum alloy with low false alarm.
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How to cite this article
You Li-Hua and Wu Jing-Jing, 2013. Generalized Likelihood Ratio Detector for Aluminum Alloy Defect Detection. Information Technology Journal, 12: 4447-4452.
DOI: 10.3923/itj.2013.4447.4452
URL: https://scialert.net/abstract/?doi=itj.2013.4447.4452
DOI: 10.3923/itj.2013.4447.4452
URL: https://scialert.net/abstract/?doi=itj.2013.4447.4452
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