An Ensemble of Supervised Learning and Image Inpainting for Mura Detection

Chia Yu Lin, Tzu Min Chang, Hao Yuan Chen, Tzer Jen Wei

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages502-505
Number of pages4
ISBN (Electronic)9798350313154
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023 - Brisbane, Australia
Duration: 10 Jul 202314 Jul 2023

Publication series

NameProceedings - 2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

Conference

Conference2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023
Country/TerritoryAustralia
CityBrisbane
Period10/07/2314/07/23

Keywords

  • Mura detection
  • SEResNeXt101
  • U-Net
  • image inpainting

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