Main-Secondary Network For Defect Segmentation Of Textured Surface Images

Building an intelligent defect segmentation system for textured images has attracted much increasing attention in both research and industrial communities, due to its significance values in the practical applications of industrial inspection and quality control. Previous models learned the classical classifiers for segmentation by designing hand-crafted features. However, defect segmentation of textured surface images poses challenges such as ambiguous shapes and sizes of defects along with varying textures and patterns in the images. Thus, hand-crafted features based segmentation methods can only be applied to particular types of textured images. To this end, it is desirable to learn a general deep learning based representation for the automatic segmentation of defects. Furthermore, it is relatively less study in efficiently extracting the deep features in the frequency domain, which, nevertheless, should be very important to understand the patterns of textured images. In this paper, we propose a novel defect segmentation deep network - Main-Secondary Network (MS-Net). Our MS-Net is trained to model both features from the spatial domain and the frequency domain, where wavelet transform is utilized to extract discriminative information from the frequency domain. Extensive experiments show the effectiveness of our MS-Net.