Study on Defection Segmentation for Steel Surface Image Based on Image Edge Detection and Fisher Discriminant

Abstract

A hybrid image segmentation method based on edge detection and Fisher discriminant is presented to detect defection, because signal-to-noise ratio of steel surface image is very low, and defection targets are small and their shape is irregular. Firstly, gradient operator detects the edge of defection image and gradient image is gotten, then grayscale of gradient image is stretched in order to enhance image contrast. Secondly, Fisher discriminant is adopted in order to find optimum threshold, meanwhile defection targets are segmented. Lastly, noise is filtered by morphology method. Defection is auto-segmented and located by this segmentation method. Experiment results show this method can detect week defection and real-time detect defection online.