@inproceedings{3d8e51a51c0d44f48ca50afc315f3962,
title = "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.",
keywords = "Mura detection, SEResNeXt101, U-Net, image inpainting",
author = "Lin, {Chia Yu} and Chang, {Tzu Min} and Chen, {Hao Yuan} and Wei, {Tzer Jen}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023 ; Conference date: 10-07-2023 Through 14-07-2023",
year = "2023",
doi = "10.1109/ICMEW59549.2023.00096",
language = "???core.languages.en_GB???",
series = "Proceedings - 2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "502--505",
booktitle = "Proceedings - 2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023",
}