An LCD Detection Method Based on the Simultaneous Automatic Generation of Samples and Masks Using Generative Adversarial Networks

Abstract

When applying deep learning methods to detect micro defects on low-contrast LCD surfaces, there are challenges related to imbalances in sample datasets and the complexity and laboriousness of annotating and acquiring target image masks. In order to solve these problems, a method based on sample and mask auto-generation for deep generative network models is proposed. We first generate an augmented dataset of negative samples using a generative adversarial network (GAN), and then highlight the defect regions in these samples using the training method constructed by the GAN to automatically generate masks for the defect images. Experimental results demonstrate the effectiveness of our proposed method, as it can simultaneously generate liquid crystal image samples and their corresponding image masks. Through a comparative experiment on the deep learning method Mask R-CNN, we demonstrate that the automatically obtained image masks have high detection accuracy.