An unsupervised generative adversarial network-based method for defect inspection of texture surfaces

Abstract

Recently, deep learning-based defect inspection methods have begun to receive more attention—from both researchers and the industrial community—due to their powerful representation and learning capabilities. These methods, however, require a large number of samples and manual annotation to achieve an acceptable detection rate. In this paper, we propose an unsupervised method of detecting and locating defects on patterned texture surface images which, in the training phase, needs only a moderate number of defect-free samples. An extended deep convolutional generative adversarial network (DCGAN) is utilized to reconstruct input image patches; the resulting residual map can be used to realize the initial segmentation defects. To further improve the accuracy of defect segmentation, a submodule termed “local difference analysis” (LDA) is embedded into the overall module to eliminate false positives. We conduct comparative experiments on a series of datasets and the final results verify the effectiveness of the proposed method.