A performance evaluation of defect detection by using Denoising AutoEncoder Generative Adversarial Networks

Abstract

In this paper, we discuss a method to detect defects in industrial products by using Denoising AutoEncoder Generative Adversarial Networks. In previous methods, a defective area is detected by restoring a defective product image which added an artificial defect to a non-defective product image by Denoising AutoEncoder (DAE). Therefore, a defective area is detected by subtracted image of them. We discuss whether further accuracy improvement is possible by introducing a framework of adversarial learning to DAE in order to restore a defective image to a non-defective image clearer.