Automatic detection of Mura defect in TFT-LCD mobile screen based on adaptive local enhancement

Miao LIAO; Yi-zhi LIU; YANG Jun-lin OU; jian-yong YU; wen-hui XIAO; Li PENG

doi:10.3788/YJYXS20183306.0475

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Automatic detection of Mura defect in TFT-LCD mobile screen based on adaptive local enhancement

Material and Device | 更新时间：2020-07-06

- Automatic detection of Mura defect in TFT-LCD mobile screen based on adaptive local enhancement
- Chinese Journal of Liquid Crystals and Displays Vol. 33, Issue 6, Pages: 475-482(2018)
- 作者机构：
  
  湖南科技大学计算机科学与工程学院, 湖南湘潭 411100
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20183306.0475
  CLC： TP391
- Received：18 January 2018，
  
  Accepted：02 April 2018，
  
  Published：2018-06
- 稿件说明：
移动端阅览
Miao LIAO, Yi-zhi LIU, YANG Jun-lin OU, et al. Automatic detection of Mura defect in TFT-LCD mobile screen based on adaptive local enhancement[J]. Chinese journal of liquid crystals and displays, 2018, 33(6): 475-482.
DOI：

Miao LIAO, Yi-zhi LIU, YANG Jun-lin OU, et al. Automatic detection of Mura defect in TFT-LCD mobile screen based on adaptive local enhancement[J]. Chinese journal of liquid crystals and displays, 2018, 33(6): 475-482. DOI： 10.3788/YJYXS20183306.0475.

摘要

针对手机屏幕图像整体亮度不均以及Mura缺陷对比度低等特点，提出一种基于自适应局部增强的Mura缺陷自动在线检测方法。首先对CCD相机采集的手机屏幕原始图像进行感兴趣区域提取、几何校正、滤波等预处理，获取图像中的屏幕区域，然后将屏幕区域划分为多个不重叠的像素块，并根据每个像素块的灰度分布特征，采用自适应局部增强算法自动识别并定位图像中的Mura区域，最后考虑到Mura缺陷大小的不确定，提出采用多层级分块的方式对屏幕区域进行检测，提高算法鲁棒性。实验结果表明，相较现有多种屏幕缺陷自动检测算法，本文方法能更准确有效地识别手机屏幕中的Mura缺陷，且覆盖率和误检率分别为91.17%和5.84%。

Abstract

Aiming at the characteristics of mobile screen image

such as non-uniform luminance and low contrast of Mura defect

an automatic on-line detection method for Mura defect based on adaptive local enhancement is proposed in this paper. First

for the original mobile screen image captured by CCD camera

preprocessing steps including region of interest extraction

geometric correction and filtering are applied to extract the screen region from the original image. Then

the screen region is divided into multiple non-overlapping pixel blocks

and the Mura regions in each block are recognized and located by adaptive local enhancement according to the intensity distribution of each block. Finally

to increase the robustness of the algorithm

a multi-level partitioning scheme is proposed in this paper to detect the Mura defects with different sizes. Experimental results show that

compared to many automatic screen defect detection methods

the proposed method can detect the Mura defects from mobile screen more accurately and effectively

and the percentages of overlapping and detection error are 91.17% and 5.84%

respectively.

关键词

Keywords

references

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