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Volume 11
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10.3390/cryst11121444
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Review

Survey of Mura Defect Detection in Liquid Crystal Displays Based on Machine Vision

by
Wuyi Ming
1,
Shengfei Zhang
1,
Xuewen Liu
2,*,
Kun Liu
1,
Jie Yuan
1,
Zhuobin Xie
1,3,
Peiyan Sun
1 and
Xudong Guo
1
1
Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China
2
School of Vehicle and Automation, Guangzhou Huaxia Vocational College, Guangzhou 510900, China
3
Guangdong Provincial Key Laboratory of Digital Manufacturing Equipment, Guangdong HUST Industrial Technology Research Institute, Dongguan 523808, China
*
Author to whom correspondence should be addressed.
Crystals 2021, 11(12), 1444; https://doi.org/10.3390/cryst11121444
Submission received: 19 October 2021 / Revised: 9 November 2021 / Accepted: 19 November 2021 / Published: 24 November 2021
(This article belongs to the Special Issue Liquid Crystals in China)
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Abstract

Liquid crystal display (LCD) is a display device based on liquid crystal electro-optic effect, and LCDs have gradually appeared and have become an indispensable part of people’s lives. In the development of LCD technology, the detection of Mura defects is a key concern in the manufacturing process. The Mura defect is a kind of display defect with low contrast and an irregular shape. This study first explains the mechanism of Mura defects in the LCD manufacturing process and classifies typical Mura defects. Then, three main purposes for the defect detection of LCDs are compared, and the advantages and disadvantages are conducted. Following that, this research examines reviews the linked literature on image preprocessing, feature extraction, dimension reduction, and classifiers of Mura defects. Finally, the future development trend and research direction of Mura defect detection based on machine vision can be drawn by this study.
Keywords: LCD; Mura defect; mechanical vision; dimension reduction; feature extraction; classifie LCD; Mura defect; mechanical vision; dimension reduction; feature extraction; classifie

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MDPI and ACS Style

Ming, W.; Zhang, S.; Liu, X.; Liu, K.; Yuan, J.; Xie, Z.; Sun, P.; Guo, X. Survey of Mura Defect Detection in Liquid Crystal Displays Based on Machine Vision. Crystals 2021, 11, 1444. https://doi.org/10.3390/cryst11121444

AMA Style

Ming W, Zhang S, Liu X, Liu K, Yuan J, Xie Z, Sun P, Guo X. Survey of Mura Defect Detection in Liquid Crystal Displays Based on Machine Vision. Crystals. 2021; 11(12):1444. https://doi.org/10.3390/cryst11121444

Chicago/Turabian Style

Ming, Wuyi, Shengfei Zhang, Xuewen Liu, Kun Liu, Jie Yuan, Zhuobin Xie, Peiyan Sun, and Xudong Guo. 2021. "Survey of Mura Defect Detection in Liquid Crystal Displays Based on Machine Vision" Crystals 11, no. 12: 1444. https://doi.org/10.3390/cryst11121444

APA Style

Ming, W., Zhang, S., Liu, X., Liu, K., Yuan, J., Xie, Z., Sun, P., & Guo, X. (2021). Survey of Mura Defect Detection in Liquid Crystal Displays Based on Machine Vision. Crystals, 11(12), 1444. https://doi.org/10.3390/cryst11121444

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