TFT-LCD表面Mura缺陷的AOI检测研究进展  

Development of AOI inspection of Mura defects on TFT-LCD surface

作　　者：陈泽康 沈奕 翟晨阳 董晨瑶 王双喜[1] CHEN Zekang;SHEN Yi;ZHAI Chenyang;DONG Chenyao;WANG Shuangxi(College of Engineering,Shantou University,Shantou 515063,China;Guangdong Provincial Key Laboratory of Automotive Display and Touch Technologies,Shantou 515041,China)

机构地区：[1]汕头大学工学院,广东汕头515063 [2]广东省车载显示触控技术重点实验室,广东汕头515041

出　　处：《液晶与显示》2024年第11期1463-1476,共14页Chinese Journal of Liquid Crystals and Displays

基　　金：广东省科技计划(No.STKJ2023070);广东省教育厅创新强校工程(No.GD20231202)。

摘　　要：液晶显示屏幕的表面缺陷检测是保证TFT-LCD等液晶显示屏质量稳定性的关键。得益于在检测表面缺陷方面高效率、低成本的优势,机器视觉技术目前已经成为TFT-LCD质量检测的主要手段。本文首先概述了液晶屏的发展历程,列举了常见Mura缺陷的类型,分别介绍了基于传统图像处理和基于深度学习的Mura缺陷检测方法,概述了图像滤波和图像亮度校正等图像预处理技术的研究动态。本文重点阐述了监督学习、无监督学习和迁移学习等人工智能技术在TFT-LCD表面Mura缺陷检测领域的应用,并对基于机器视觉的TFT-LCD表面Mura缺陷检测的技术发展趋势进行了展望。Surface defect inspection is crucial for ensuring the quality stability of liquid crystal display(LCD)screens,particularly for TFT-LCDs.Known for its high efficiency and low cost,machine vision technology has become the primary means for inspecting TFT-LCD quality.This paper reviews the development of LCD and lists the common types of Mura defects.The traditional image processing methods and deep learning for detecting Mura defects are introduced,and recent advancements in image preprocessing techniques such as image filtering and brightness correction are summarized.The application of artificial intelligence techniques,such as supervised learning,unsupervised learning and transfer learning is introduced,in the detection of Mura defects on TFT-LCD surfaces.Finally,the research directions for machine vision-based Mura defect inspection technology on TFT-LCD are anticipated.

关 键 词：TFT-LCD Mura缺陷 机器视觉 图像处理 深度学习 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]