Image-Based Scratch Detection by Fuzzy Clustering and Morphological Features

Detection of scratch defects on randomly textured surfaces remains challenging due to their unnoticeable visual features. In this paper, an algorithm for piezoelectric ceramic plate surface scratch defects based on the combination of fuzzy c-means clustering and morphological features is proposed. Foreground membership of each gray value is calculated firstly on a reference set of training images by fuzzy c-means clustering and the interpolation method, then an enhanced image is obtained by multiplying the foreground membership function and gray image. The location relationship between regions and the gradient direction of regions is extracted from the binary image of the enhanced image. Based on the morphological features, isolated non-scratched defects are filtered out and the intermittent scratches are merged. Experiments show that the algorithm can be used to detect scratch defects on the surface of a piezoelectric ceramics plate with randomly textured surfaces.