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 Yazhou1,2, LU Xianling1,2   

  1. 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).


张亚洲1,2, 卢先领1,2   

  1. 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:



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)



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: