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广东工业大学硕士学位论文（工程硕士）基于机器视觉的手机屏幕玻璃缺陷检测方法研究萧显二〇一九年五月。

分类号：学校代号：11845 UDC：密级：学号：2111601243 广东工业大学硕士学位论文（工程硕士）基于机器视觉的手机屏幕玻璃缺陷检测方法研究萧显指导教师姓名、职称：袁清珂教授校外导师姓名、职称：梅领亮工程师学科(专业)或领域名称：机械工程学生所属学院：机电工程学院论文答辩日期： 2019 年 5 月

A Dissertation Submitted to Guangdong University of Technology for the Degree of Master (Master of Engineering) Research on Detection Method of Cellphone Screen Glass Defect Based on Machine Vision Master Candidate:Xiao Xian Supervisor:Prof. Yuan Qingke May 2019 School of Electromechanical Engineering Guangdong University of Technology Guangzhou, Guangdong, P. R. China, 510006

广东工业大学硕士学位论文摘要在移动终端设备越来越普及的发展趋势下，数码产品如笔记本电脑、相机、平板电脑等使用频率越来越高，手机更是在人们日常生活中占有不可或缺的重要地位。触摸屏作为手机的重要构成部分，屏幕玻璃的质量好坏直接关系到手机的正常使用，玻璃盖板上的缺陷会影响其机械特性及光学性质，严重破坏产品使用价值。因此，对玻璃盖板生产环节中的瑕疵缺陷检测是十分重要的。且针对目前手机产品更新换代频率越来越高，生产制造需求量大的背景下，传统人工检测无法满足，应用机器视觉代替传统方法也是目前工业生产的发展趋势。针对以上情况现状，本文对玻璃盖板生产流程和缺陷检测标准进行分析研究，对常见缺陷类型和现有检测理论进行讨论，设计了玻璃盖板缺陷检测系统的总体方案；对比分析了不同预处理方法在玻璃缺陷上的应用效果，对玻璃边缘缺陷及内部缺陷检测分类算法进行深入的研究讨论，并完成相关仿真实验测试。本文的主要研究内容如下：（1）针对手机玻璃盖板生产工艺流程和缺陷检测标准要求，基于缺陷图像数据特点分析，构建了玻璃缺陷检测系统。系统由光源成像系统、图像采集装置、图像处理系统、系统软件等部分组成。（2）对比分析当前常用的图像预处理技术，基于图像平滑滤波、形态学操作、边缘检测算法的讨论分析，分别针对盖板玻璃边缘缺陷和内部缺陷的特点，采用对应合适的预处理方法，并在实际样本上做仿真实验，得出结果。（3）对于玻璃边缘的崩边缺陷，经过预处理后，采用行列像素遍历搜索的方法得出边缘数据点集，对比最小二乘法和随机抽样一致的仿真实验效果，最终选择随机抽样一致方法拟合出边缘直线模型，根据实际数据与拟合模型偏差判断出边缘缺陷区域。（4）基于玻璃内部缺陷的特性，探讨并提取出缺陷图像的形态学特征和不变矩特征；为了得到更好的训练模型，讨论svm方法的的参数选择和优化，提出讨论解决样本不均匀的方法，有效提高分类的准确率。关键词：缺陷检测；机器视觉；图像处理；遍历搜索；特征提取 Ⅰ

ABSTRACT ABSTRACT Under the trend of increasing popularity of mobile terminal devices, digital products such as notebook computers, cameras, and tablets are becoming more and more frequently used, and mobile phones are indispensable in people's daily lives. The touch scree n is an important part of the mobile phone. The quality of the screen glass is directly related to the normal use of the mobile phone. The defects on the glass cover will affect its mechanical and optical properties and seriously damage the use value of th e product. Therefore, it is very important to detect defects in the production of glass cover. In view of the fact that the current frequency of mobile phone product replacement is getting higher and higher, and the demand for manufacturing is large, traditional manual testing cannot be satisfied. The application of machine vision instead of the traditional method is also the development trend of industrial production. In view of the above situation, this paper analyzes the glass cover production process and defect detection standards, discusses the common defect types and existing detection theories, designs the overall scheme of the glass cover defect detection system, and compares the different pretreatments. The application of the method on glass defects, in-depth research and discussion on the glass edge defect and internal defect detection classification algorithm, and complete the relevant simulation test. The main research contents of this paper are as follows. (1) According to the production process flow and defect detection standard requirements of mobile glass cover, based on the analysis of defect image data characteristics, a glass defect detection system was constructed. The system consists of a light source imaging system, an image acquisition device, an image processing system, and system software. (2) Contrasting and analyzing the currently used image preprocessing techniques, based on the analysis and analysis of image smoothing filtering, morphological operations and edge detection algorithms, respectively, corresponding to the characteristics of edge defects and internal defects of the cover glass, corresponding to appropriate pretreatment methods, And do simulation experiments on the actual samples, and the results are Ⅲ

广东工业大学硕士学位论文 obtained. (3) For the chipping defects of the glass edge, after pre-processing, the edge data point set is obtained by the method of row and column pixel traversal search, and the simulation experiment results which are consistent with the least squares method and the random sampling are compared, and finally the random sampling consistent method is selected. The edge straight line model is combined, and the edge defect area is determined according to the deviation between the actual data and the fitted model. (4) Based on the characteristics of internal defects of glass, the morphological features and invariant moment features of the defect image are discussed and extracted. In order to get a better training model, the parameter selection and optimization of svm method are discussed, and discussion is made to solve the sample non-uniformity. The method effectively improves the accuracy of the classification. Keyword: defect detection,machine vision,image Processing, traversal,feature extraction Ⅱ

目录目录摘要 ................................................................................................................................... I ABSTRACT ....................................................................................................................... II 目录 .................................................................................................................................. IV CONTENTS .................................................................................................................... VII 第一章绪论 ....................................................................................................................... 1 1.1 机器视觉研究状况 .............................................................................................. 1 1.2 本课题研究背景与意义 ...................................................................................... 2 1.3 国内外研究现状 .................................................................................................. 4 1.3.1 国内外研究现状及分析 ........................................................................... 4 1.3.2 国外研究现状 ........................................................................................... 5 1.3.3 国内研究现状 ........................................................................................... 6 1.4 课题主要研究内容和结构框架 .......................................................................... 6 1.5 本章总结 .............................................................................................................. 7 第二章玻璃缺陷检测系统的总体设计方案 ................................................................... 8 2.1 玻璃盖板生产环节概述 ...................................................................................... 8 2.1.1 玻璃盖板生产流程与工艺分析 ............................................................... 8 2.1.2 玻璃盖板缺陷造成原因及检验标准 ..................................................... 11 2.2 缺陷检测系统设计 ............................................................................................ 13 2.3 本章小结 ............................................................................................................ 16 第三章玻璃边缘检测算法设计 ..................................................................................... 18 3.1 图像预处理分析 ................................................................................................ 18 3.1.1 玻璃图像滤波算法 ................................................................................. 18 3.1.2 玻璃图像形态学操作 ............................................................................. 22 Ⅳ

广东工业大学硕士学位论文 3.1.3 滤波处理结果 ......................................................................................... 24 3.2 玻璃崩边检测方法 ............................................................................................ 24 3.2.1 边缘轮廓区域提取概述 ......................................................................... 24 3.2.2 常用图像配准方法 ................................................................................. 25 3.2.3 轮廓相似度测度 ..................................................................................... 27 3.2.4 差影运算及缺陷分割 ............................................................................. 29 3.3 基于搜索线的边缘缺陷分割 ............................................................................ 30 3.3.1 边缘线上标记点提取 ............................................................................. 30 3.3.2 标记点直线拟合方法 ............................................................................. 31 3.3.3 缺陷判断准则 ......................................................................................... 35 3.4 本章小结 ............................................................................................................ 37 第四章盖板玻璃表面缺陷分类方法 ............................................................................. 39 4.1 盖板玻璃表面缺陷分割 ..................................................................................... 39 4.1.1 基于模板匹配的表面缺陷提取 ............................................................. 39 4.1.2 同一区域缺陷合并 ................................................................................. 40 4.2 缺陷图像的特征提取 ........................................................................................ 41 4.2.1 缺陷分类特性 .......................................................................................... 41 4.2.2 缺陷图像的特征参数介绍 ..................................................................... 42 4.3 基于 SVM 的表面缺陷分类 .............................................................................. 46 4.3.1 SVM（支持向量机）概述 ..................................................................... 46 4.3.2 SVM 参数选择 ........................................................................................ 49 4.3.3 C-SVM 的参数设计及优化 .................................................................... 52 4.3.4 本文 SVM 缺陷分类模型训练过程....................................................... 53 4.3.5 实验结果与分析 ..................................................................................... 56 4.4 缺陷识别分类软件界面介绍及其实现 ............................................................ 57 4.5 本章总结 ............................................................................................................ 58 总结与展望 ....................................................................................................................... 60 参考文献 ........................................................................................................................... 63 · Ⅵ

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基于机器视觉的手机屏幕玻璃缺陷检测方法研究.pdf

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