Surface defect detection method of light-guide plate based on improved coherence enhancing diffusion and texture energy measure-Gaussian mixture model

doi:10.11772/j.issn.1001-9081.2019091519

Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (5): 1545-1552.DOI: 10.11772/j.issn.1001-9081.2019091519

• Frontier & interdisciplinary applications • Previous Articles

Surface defect detection method of light-guide plate based on improved coherence enhancing diffusion and texture energy measure-Gaussian mixture model

ZHANG Yazhou^1,2, LU Xianling^1,2

1.School of Internet of Things Engineering, Jiangnan University, WuxiJiangsu 214122, China
2.Key Laboratory of Advanced Process Control for Light Industry,Ministry of Education (Jiangnan University), WuxiJiangsu 214122, China

Received:2019-09-03 Revised:2019-10-22 Online:2020-05-10 Published:2020-05-15
Contact: LU Xianling, born in 1972, Ph. D., professor. His research interests include machine vision, big data.
About author:ZHANG Yazhou, born in 1990, M. S. candidate. His research interests include machine vision, pattern recognition, artificial intelligence.LU Xianling, born in 1972, Ph. D., professor. His research interests include machine vision, big data.
Supported by:
This work is partially supported by the Ministry of Education Science and Technology Development Center “Cloud Data Fusion, Science and Education Innovation” Fund (2017A13055).

基于改进相干增强扩散与纹理能量测度和高斯混合模型的导光板表面缺陷检测方法

张亚洲^1,2, 卢先领^1,2

1.江南大学物联网工程学院,江苏无锡 214122
2.轻工过程先进控制教育部重点实验室(江南大学),江苏无锡 214122

通讯作者: 卢先领(1972—)
作者简介:张亚洲(1990—),男,河南开封人,硕士研究生,主要研究方向:机器视觉、模式识别、人工智能；　卢先领(1972—),男,江苏无锡人,教授,博士,主要研究方向:机器视觉、大数据。
基金资助:
教育部科技发展中心“云数融合科教创新”基金资助课题(2017A13055)。

Abstract

Abstract:

Existing Liquid Crystal Display (LCD) light-guide plate surface defect detection methods have high missing rate and false detection rate as well as low adaptability to the complex texture structure with gradual change of product surface. Therefore, a LCD light-guide plate surface defect detection method was proposed based on Improved Coherence Enhancing Diffusion (ICED) and Texture Energy Measure-Gaussian Mixture Model (TEM-GMM). Firstly, an ICED model was established, Mean Curvature Flow (MCF) filter was introduced based on the structure tensor, so that the Coherence Enhancing Diffusion (CED) model had better retention effect on the edge of the thin-line defect, and the texture-enhanced and background texture-suppressed filtered image was obtained by using coherence. Then, the texture features of the image were extracted based on the Laws Texture Energy Measure (TEM), and with the texture features of background as the training data for the Gaussian Mixture Model (GMM), the parameters of GMM were estimated by Expectation Maximization (EM) algorithm. Finally, the posterior probability of each pixel in the target image was calculated and used to judge the defect pixel at the online detection stage. The experimental results show that compared with other methods, the missing rate and false detection rate of this method in the distribution of the light-guide particles randomly and the regularly distributed defect image test datasets was 3.27%, 4.32% and 3.59%, 4.87% respectively. The proposed detection method has an extensive application scope, and can effectively detect defects such as scratches, foreign objects, dirt and crushing on the surface of LCD light-guide plate.

Key words: machine vision, defect detection, Mean Curvature Flow (MCF), Coherence Enhancing Diffusion (CED), Texture Energy Measure (TEM)

摘要：

针对液晶屏(LCD)导光板表面缺陷检测方法存在漏检率和误检率较高,对产品表面复杂渐变的纹理结构适应性差的问题,提出一种基于改进相干增强扩散(ICED)与纹理能量测度和高斯混合模型(TEM-GMM)的LCD导光板表面缺陷检测方法。首先,构建ICED模型,基于结构张量引入平均曲率流扩散(MCF)滤波,使得相干增强扩散(CED)模型对缺陷的细线状纹理有良好的边缘保持效果,并利用相干性得到缺陷纹理增强和背景纹理抑制的滤波后图像;然后,根据Laws纹理能量测度(TEM)提取图像纹理特征,将图像的背景纹理特征作为离线阶段高斯混合模型(GMM)的训练数据,使用期望最大化(EM)算法估计GMM参数;最后,计算待检测图像各像素的后验概率,并将其作为在线检测阶段缺陷像素的判断依据。实验结果表明,该检测方法在导光颗粒随机、规则两种分布的缺陷图像测试数据组上的漏检率和误检率分别为3.27%、4.32%和3.59%、4.87%。所提检测方法适用范围广,可有效检测出LCD导光板表面划痕、异物、脏污和压伤等类型的缺陷。

关键词: 机器视觉, 缺陷检测, 平均曲率流, 相干增强扩散, 纹理能量测度

CLC Number:

TP181

ZHANG Yazhou, LU Xianling. Surface defect detection method of light-guide plate based on improved coherence enhancing diffusion and texture energy measure-Gaussian mixture model[J]. Journal of Computer Applications, 2020, 40(5): 1545-1552.

张亚洲, 卢先领. 基于改进相干增强扩散与纹理能量测度和高斯混合模型的导光板表面缺陷检测方法[J]. 计算机应用, 2020, 40(5): 1545-1552.

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Surface defect detection method of light-guide plate based on improved coherence enhancing diffusion and texture energy measure-Gaussian mixture model

基于改进相干增强扩散与纹理能量测度和高斯混合模型的导光板表面缺陷检测方法

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