Image Inpainting with Self-Supervised Learning for Mura Detection System

Abstract

Mura is usually caused by inhomogeneity and material defects in the manufacturing process. According to the JND value, it can be divided into light Mura and serious Mura. In order to optimize the repair process, the factory hopes to distinguish between light Mura and serious Mura before sending them to the repair site. However, the traditional AI model only distinguishes between normal and Mura and is ineffective in distinguishing between light Mura and serious Mura. To address this issue, we propose a Mura Detection System using an image inpainting model with a self-supervised technique and an attention module to distinguish light Mura and serious Mura. The experiment results show that the proposed method’s Area Under Curve (AUC) can reach 0.854.