An Ensemble of Supervised Learning and Image Inpainting for Mura Detection

Abstract

Mura refers to surface defects or areas of uneven brightness that can occur during factory panel production. Mura can vary in size and shape and be categorized as “light Mura” or “serious Mura.” To optimize the repair process, factories aim to differentiate between the two types of Mura before sending the panels for repair. However, current Mura detection models focus only on identifying “nrmal” and “Mura,” resulting in poor performance in distinguishing between light and serious Mura. To address this issue, we propose an ensemble approach called the Ensemble Image Inpainting and Supervised Modeling Mura Detection System (EISMDS), which combines supervised and image inpainting models to differentiate between the two types of Mura. Experimental results show that our approach improves the True Positive Rate (TPR) by 11 % under a high True Negative Rate (TNR) compared to a single supervised detection model.