@inproceedings{c0b3af46b165496eb9bd5aedf4eff2c7,
title = "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.",
author = "Chang, {Tzu Min} and Chen, {Hao Yuan} and Lin, {Chia Yu}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 ; Conference date: 17-07-2023 Through 19-07-2023",
year = "2023",
doi = "10.1109/ICCE-Taiwan58799.2023.10227069",
language = "???core.languages.en_GB???",
series = "2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "339--340",
booktitle = "2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings",
}