化工学报 ›› 2018, Vol. 69 ›› Issue (3): 1238-1243.DOI: 10.11949/j.issn.0438-1157.20171486

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基于变异系数法的工业产品表面缺陷快速检测应用研究

李澄非, 田果, 董超俊, 吉登清   

  1. 五邑大学信息工程学院, 广东 江门 529020
  • 收稿日期:2017-11-07 修回日期:2017-11-21 出版日期:2018-03-05 发布日期:2018-03-05
  • 通讯作者: 李澄非
  • 基金资助:

    2012广东省科技厅第一批产业技术研究与开发资金计划项目(2012B010100016);2015广东省教育厅特色创新类项目(2015GXJK148,2015GXJK151);2015广东省研究生教育创新计划项目(2015SFKC39);2017年广东省教育厅—思科公司产学合作协同育人项目(粤教高[2017]153号);五邑大学2017本科教学质量与教学改革工程建设项目(JX2017001);2017年广东省科技发展专项资金(2017A010101019,2017A010101034)。

Application of variation coefficient to fast detection on surface defects of industrial products

LI Chengfei, TIAN Guo, DONG Chaojun, JI Dengqing   

  1. Wuyi University, School of Information Engineering, Jiangmen 529020, Guangdong, China
  • Received:2017-11-07 Revised:2017-11-21 Online:2018-03-05 Published:2018-03-05
  • Supported by:

    supported by the First Batch of Industrial Technology Research and Development Funds Plan Project of Department of Science and Technology of Guangdong(2012B010100016), the Characteristics Innovation Projects of Department of Education of Guangdong (2015GXJK148, 2015GXJK151), the Graduate Education Innovation Project of Department of Education of Guangdong(2015SFKC39), the Department of Education-Cisco Company Production and Cooperation Education Project of Guangdong (2017, No.153), the 2017 Undergraduate Teaching Quality and Teaching Reform Project Construction Project of Wuyi University(JX2017001) and the 2017 Special Project for Science and Technology Development of Guangdong (2017A010101019, 2017A010101034).

摘要:

为了提高工业产品质量控制中表面缺陷检测的准确性和快速性,提出了一种基于机器视觉的工业产品表面缺陷快速检测方法。方法引用变异系数的概念,通过待检测图像和模板图像进行差运算得到差影图像,并通过计算其变异系数以确定阈值,利用分割定位缺陷,从而实现缺陷的快速检测。壁纸表面缺陷检测实验结果表明,本方法提高了图像检测系统的准确性和鲁棒性,误检率大大降低。

关键词: 缺陷检测, 机器视觉, 变异系数, 差影图像

Abstract:

To improve accuracy and speed of quality control detection on surface defects of industrial products, a novel fast detection approach was proposed based on machine visualization. With introduction of variation coefficient, a difference image was obtained by image differencing analysis of the test image and the model image. Threshold was determined by variation coefficient of the difference image and then defects were located by image dicing. Experimental results on wallpaper surface defect detection show that the proposed method improved accuracy and robustness of image detection and reduced false detection rate greatly.

Key words: defect detection, machine visualization, coefficient of variation, difference image

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