Low-contrast surface inspection of mura defects in liquid crystal displays using optical flow-based motion analysis

Abstract

This paper proposes a machine vision scheme for mura defect detection in LCD manufacturing. Mura is a Japanese word for blemish, which typically shows brightness imperfections from its surroundings in the surface. It appears as a low-contrast region without clear edges. Traditional automatic visual inspection algorithms detect mura defects from individual still images. They neglect that a mura defect may not be visually sensed in the image from a stationary system. In this study, the LCD panel is assumed to move along a track. While the panel passes through a fixed camera, the light reflection from different angles can effectively enhance the mura defect in the low-contrast images. The mura detection problem is therefore treated as a motion analysis in image sequences using optical flow techniques. Since a LCD panel moves along a single direction, both two-dimensional and one-dimensional optical flow methods are developed. Three discriminative features based on flow magnitude, mean flow magnitude and flow density in the optical flow field are presented to extract the defective regions. Both real panel images and synthetic surface images are used to evaluate the efficacy of the proposed methods. Experimental results have shown that the proposed 1D optical flow method works as well as the 2D optical flow method to detect very low-contrast mura defects of small size, and achieves a high processing rate around 20 frames per second for images of size 200 × 200.