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基于改进Otsu算法的TFT-LCD点缺陷自动光学检测系统

Automatic optical detection system for TFT-LCD spot-type defect based on improved Otsu algorithm

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摘要

针对TFT-LCD点缺陷自动光学检测时, 缺陷与背景对比度较低难以用传统阈值分割算法处理的难题, 提出一种改进的Otsu算法, 并构建了TFT-LCD 点缺陷自动光学检测系统。首先, 通过Gabor滤波去除了纹理背景的影响。然后, 利用威布尔函数形态参数分段取值时, 其分布函数呈现的不同分布特性, 改进了传统Otsu阈值提取函数。最后, 进行了离线测试试验和在线测试试验。试验表明, 改进的Otsu算法在点缺陷与背景对比度较低的情况下分割效果优于传统Otsu算法。将该算法移植到TFT-LCD 点缺陷自动光学检测硬件平台上进行在线测试, 正确检测率可达到94%, 单个样本最短检测时间可缩短至150 ms。降低了TFT-LCD人工检测点缺陷的工作量和劳动强度。

Abstract

In order to solve the problem that the traditional threshold segmentation algorithm is difficult to detect the spot-type defect from low contrast background, an improved Otsu algorithm is proposed. Based on this, a TFT-LCD spot-type defect automatic optical detection system is constructed. First, the effect of texture background is removed by Gabor filtering. Then, Using the different distribution characteristics of Weibull distribution function when the shape parameter of the Weibull function is segmented, the threshold extraction function of traditional Otsu is improved. Finally, offline test and online test are carried out.The experiment shows that the improved Otsu algorithm can produce better detection result than the traditional Otsu algorithm in the case of low contrast between defects and background. The algorithm is transplanted to the TFT-LCD spot-type defect automatic optical detection hardware platform to test. The correct detection rate can reach 94% and the detection time can be shortened to 150 ms. It reduces the workload and labor intensity of TFT-LCD manual spot-type defect detection.

Newport宣传-MKS新实验室计划
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中图分类号:TP278

DOI:10.3788/yjyxs20183303.0221

所属栏目:图像处理

基金项目:国家自然科学基金 (No.61472173); 江西省科技厅重点研发计划(No.20151BBE50083)

收稿日期:2017-10-15

修改稿日期:2017-12-14

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作者单位    点击查看

郭 波:南昌工程学院 江西省精密驱动与控制重点实验室,江西 南昌 330099
管菊花:江西机电职业技术学院,江西 南昌 330013
黄志开:南昌工程学院 江西省精密驱动与控制重点实验室,江西 南昌 330099

联系人作者:郭波(guobo651@126.com)

备注:郭波(1981-), 男, 山东滨州人, 博士, 讲师, 2003年于南昌航空大学获得学士学位, 2010年于西北工业大学获得硕士学位,2016年于华南理工大学获得博士学位, 主要从事机器视觉及自动化方面的研究。

【1】毕昕, 丁汉.TFT-LCD Mura缺陷机器视觉检测方法 [J].机械工程学报, 2010, 46(12): 13-19.
BI X, DING H. Machine vision inspection method of Mura defect for TFT-LCD [J]. Journal of Mechanical Engineering, 2010, 46(12): 13-19. (in Chinese)

【2】李晓吉, 赵彦礼, 栗鹏, 等.TFT-LCD改善“斑点不良”的研究 [J].液晶与显示, 2017, 32(7): 507-511.
LI X J, ZHAO Y L, LI P, et al. Prevention of TFT-LCD''s "Spot issue" [J]. Chinese Journal of Liquid Crystals and Displays, 2017, 32(7): 507-511. (in Chinese)

【3】曾彥馨.用独立成份分析法于薄膜电晶体液晶显示面板之制程监控与表面瑕疵检测 [D].台湾: 元智大學, 2013.
TSENGY H. Independent component analysis approaches for process variation monitoring and Mura defect inspection in TFT-LCD manufacturing [D]. Taiwan: Yuan Ze University, 2013. (in Chinese)

【4】CHEN S L, CHOU S T. TFT-LCD Mura defect detection using wavelet and cosine transforms [J]. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2008, 2(3): 441-453.

【5】TSAI D M, TSAI H Y. Low-contrast surface inspection of Mura defects in liquid crystal displays using optical flow-based motion analysis [J]. Machine Vision and Applications, 2011, 22(4): 629-649.

【6】张腾达, 卢荣胜, 党学明.TFT-LCD表面缺陷检测中一维DFT方法中邻域r的自动选取 [J].中国机械工程, 2016, 27(21): 2895-2901.
ZHANG T D, LU R S, DANG X M. Automatic neighbor r selection for one-dimensional DFT method in the surface defect inspection of TFT-LCD [J]. China Mechanical Engineering, 2016, 27(21): 2895-2901. (in Chinese)

【7】张腾达, 卢荣胜.自动周期选取的DFT方法在TFT-LCD平板检测中的应用 [J].电子测量与仪器学报, 2016, 30(3): 361-373.
ZHANG T D, LU R S. Automatic period selection for DFT method in the application of TFT-LCD panel detection [J]. Journal of Electronic Measurement and Instrumentation, 2016, 30(3): 361-373. (in Chinese)

【8】LEE J Y, KIM T W, PAHK H J. Robust defect detection method for a non-periodic TFT-LCD pad area [J]. International Journal of Precision Engineering and Manufacturing, 2017, 18(8): 1093-1102.

【9】毕昕.面向TFT-LCD制程的Mura缺陷机器视觉检测方法研究 [D].上海: 上海交通大学, 2009.
BI X. Study on the methods of machine vision inspection for the Mura defect of TFT-LCD process [D]. Shanghai: Shanghai Jiao Tong University, 2009. (in Chinese)

【10】陈燕芹, 段锦, 祝勇, 等.基于纹理特征的图像复杂度研究 [J].中国光学, 2015, 8(3): 407-414.
CHEN Y Q, DUAN J, ZHU Y, et al. Research on the image complexity based on texture features [J]. Chinese Optics, 2015, 8(3): 407-414. (in Chinese)

【11】MALARVEL M, SETHUMADHAVAN G, BHAGI P C R, et al. An improved version of Otsu''s method for segmentation of weld defects on X-radiography images [J]. Optik-International Journal for Light and Electron Optics, 2017, 142: 109-118.

【12】XU X Y, XU S Z, JIN L H, et al. Characteristic analysis of Otsu threshold and its applications [J]. Pattern Recognition Letters, 2011, 32(7): 956-961.

【13】杨名宇, 李刚.利用区域信息的航拍图像分割 [J].中国光学, 2014, 7(5): 779-785.
YANG M Y, LI G. Aerial image segmentation with region information [J]. Chinese Optics, 2014, 7(5): 779-785. (in Chinese)

引用该论文

GUO Bo,GUAN Ju-hua,HUANG Zhi-kai. Automatic optical detection system for TFT-LCD spot-type defect based on improved Otsu algorithm[J]. Chinese Journal of Liquid Crystals and Displays, 2018, 33(3): 221-227

郭 波,管菊花,黄志开. 基于改进Otsu算法的TFT-LCD点缺陷自动光学检测系统[J]. 液晶与显示, 2018, 33(3): 221-227

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