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

doi:10.11949/j.issn.0438-1157.20171486

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

Previous Articles Next Articles

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

LI Chengfei, TIAN Guo, DONG Chaojun, JI Dengqing

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).

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

五邑大学信息工程学院, 广东江门 529020

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）。

Abstract

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

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

CLC Number:

TP274

LI Chengfei, TIAN Guo, DONG Chaojun, JI Dengqing. Application of variation coefficient to fast detection on surface defects of industrial products[J]. CIESC Journal, 2018, 69(3): 1238-1243.

李澄非, 田果, 董超俊, 吉登清. 基于变异系数法的工业产品表面缺陷快速检测应用研究[J]. 化工学报, 2018, 69(3): 1238-1243.

References 32

[1]	IPUNG H P, TJANDRASA H. Urban road materials identification using narrow near infrared vision system[J]. International Journal of Electrical & Computer Engineering, 2017, 7(3):1171-1179.
[2]	YOU F, LI Y H, HUANG L, et al. Monitoring drivers' sleepy status at night based on machine vision[J]. Multimedia Tools & Applications, 2017, 76(13):1-18.
[3]	CHU M, GONG R, WANG A. Strip steel surface defect classification method based on enhanced twin support vector machine[J]. Transactions of the Iron & Steel Institute of Japan, 2014, 54(1):119-124.
[4]	ZHOU J, ZHANG R. Classification of apple surface defect based on active thermal imaging technology[J]. Journal of East China Jiaotong University, 2012, 9(4):221-224.
[5]	ZHANG Z, NAKAMURA I, LI C J. Metal plating surface defect detection by template matching using morpholocal processing[J]. Image & Vision Computing, 2011, 22(14):1157-1164.
[6]	ZHANG H, XIE F, CAO M, et al. A steel ball surface quality inspection method based on a circumferential eddy current array sensor[J]. Sensors, 2017, 17(7):1536-1537.
[7]	HE B, ZHANG Y, TONG K J, et al. Curved surface imaging theory and defect detection for sealing rings used in aerospace systems[J]. Optics & Precision Engineering, 2015, 23(11):3051-3060.
[8]	WU Z, SUN C, LIU J. Oil-seal surface defect automatic detection and recognition method based on image processing[J]. Chinese Journal of Scientific Instrument, 2013, 34(5):1093-1099.
[9]	OUYANG Q, ZHANG L Z, ZHAO L M, et al. Experimental study on quantitative surface defect depth detection based on laser scanning technology in continuous casting[J]. Ironmaking & Steelmaking, 2011, 38(5):363-368.
[10]	HAO X, BIN L I. Detection of rail track surface defects based on machine vision[J]. Journal of Beijing Jiaotong University, 2015, 39(2):28-34.
[11]	ZHANG Y, LI T, LI Q. Defect detection for tire laser shearography image using curvelet transform based edge detector[J]. Optics & Laser Technology, 2013, 47(4):64-71.
[12]	SIOD?AK D. Building large molecular models with plastic screw-on bottle caps and sturdy connectors[J]. Journal of Chemical Education, 2017, 94(2):256-259.
[13]	KO J S, RHEEM J Y. Defect detection of polycrystalline solar wafers using local binary mean[J]. International Journal of Advanced Manufacturing Technology, 2016, 82(9/10/11/12):1753-1764.
[14]	CHENG X, TAO Z, HUANG J. System design of containment external defects detection[J]. Journal of Geomatics, 2017, 42(2):71-75.
[15]	王震宇. 基于机器视觉钢板表面缺陷检测技术研究[J]. 计算机与现代化, 2013, (7):130-134. WANG Z Y. Research on surface defect detection technology based on machine vision[J]. Computer and Modernization, 2013, (7):130-134.
[16]	CAI Z, XIAO J, WEN L, et al. Algorithm of defect segmentation for AFP based on prepregs[J]. Journal of Aeronautical Materials, 2017, 37(2):21-27.
[17]	LIU L, ZENG L. Defect segmentation in digital radiography images of castings on a railway freight car[J]. Insight:Non-Destructive Testing and Condition Monitoring, 2011, 53(7):372-376.
[18]	FALQUE R, VIDALCALLEJA T, MIRO J V. Defect detection and segmentation framework for remote field eddy current sensor data[J]. Sensors, 2017, 17(10):2276-2278.
[19]	YONG D C. Automated inspection algorithm for thick plate using dual light switching lighting method[J]. World Academy of Science Engineering & Technology, 2012, 6(12):1475-1478.
[20]	BAI X, WANG P, ZHOU F. Pedestrian segmentation in infrared images based on circular shortest path[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(8):2214-2222.
[21]	YASNOFF W A. Error measures for scene segmentation in automated cytology[J]. Pattern Recognition, 1977, 9(4):217-231.
[22]	DE SOLORZANO C O, CHIN K, CHOU W S, et al. Measurement of genetic instability in breast cancer by confocal microscopy and 3D image analysis[J]. Annual International Conference of the IEEE Engineering in Medicine and Biology. 1999, 2(2):822-826.
[23]	马义德, 苏茂君, 陈锐. 基于PCNN的图像二值化及分割评价方法[J]. 华南理工大学学报(自然科学版), 2009, 37(5):49-53. MA Y D, SU M J, CHEN R. Image binarization and segmentation evaluation method based on PCNN[J]. Journal of South China University of Technology(Natural Science Edition), 2009, 37(5):49-53.
[24]	闵超波, 张俊举, 常本康, 等. 无需先验知识的红外显著性目标分割评价准则[J]. 模式识别与人工智能, 2013, 26(12):1106-1114. MIN C B, ZHANG J J, CHANG B K, et al. No prior knowledge of infrared significance target segmentation evaluation criterion[J]. Pattern Recognition and artificial Intelligence, 2013, 26(12):1106-1114.
[25]	WEBER E U, SHAFIR S, BLAIS A R. Predicting risk sensitivity in humans and lower animals:risk as variance or coefficient of variation[J]. Psychological Review, 2004, 111(2):430-431.
[26]	王义文, 屈冠彤, 刘献礼, 等. 钢球表面缺陷的图像差分检测算法[J]. 计算机辅助设计与图形学学报, 2016, 28(10):1699-1704. WANG Y W, QU G D, LIU X L, et al. Image difference detection algorithm for steel ball surface defects[J]. Journal of Computer Aided Design and Graphics, 2016, 28(10):1699-1704.
[27]	MAZHIR S N, ALI A H, HADI F W, et al. Studying the effect of dielectric barrier discharges on the leukemia blood cells using digital image processing[J]. Iosr Journal of Pharmacy & Biological Sciences, 2017, 12(2):6-12.
[28]	DAVROS W. Digital image processing for medical applications[J]. Medical Physics, 2010, 37(2):948-949.
[29]	王平, 魏征, 崔卫红, 等. 一种基于统计学习理论的最小生成树图像分割准则[J]. 武汉大学学报(信息科学版), 2017, 42(7):877-883. WANG P, WEI Z, CUI W H, et al. A minimum spanning tree image segmentation criterion based on statistical learning theory[J]. Journal of Wuhan University (Information Science), 2017, 42(7):877-883.
[30]	施铃泉, 蒋爱民, 朱昌平, 等. 基于稀疏表示的离散系数FIR滤波器设计[J]. 科学技术与工程, 2014, 14(1):205-208. SHI L Q, JIANG A M, ZHU C P, et al. Design of FIR filters with discrete coefficients based on sparse representation[J]. Science Technology and Engineering, 2014, 14(1):205-208.
[31]	TOLBA A S. Fast defect detection in homogeneous flat surface products[J]. Expert Systems with Applications, 2011, 38(10):12339-12347.
[32]	TSAI D M, CHEN M C, LI W C, et al. A fast regularity measure for surface defect detection[J]. Machine Vision & Applications, 2012, 23(5):869-886.

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

基于变异系数法的工业产品表面缺陷快速检测应用研究

PDF (PC)

Knowledge

Cited

Abstract

Cite this article

share this article

References 32

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	WENG Runying, SUN Bin, ZHAO Yuxiao, ZHANG Jingyue, WEN Yingjie. Flow pattern recognition method of gas-liquid two-phase flow based on adaptive optimal kernel and convolution neural network [J]. CIESC Journal, 2018, 69(12): 5065-5072.
[2]	CHU Fei, CHENG Xiang, DAI Wei, ZHAO Xu, WANG Fuli. Prediction approach for terminal batch process quality based on process transfer [J]. CIESC Journal, 2018, 69(6): 2567-2575.
[3]	JIANG Xinyi, DU Hongbin, LI Shaojun. Atmospheric tower soft sensor based on regression and mutual information of kernel slow features [J]. CIESC Journal, 2017, 68(5): 1977-1986.
[4]	LIU Ruilan, LIU Shuyun, RONG Zhou, JIANG Bing, PANG Zongqiang. Modeling soft sensor of 4-CBA concentration by AdaBoost algorithm with dual threshold technique [J]. CIESC Journal, 2017, 68(5): 2009-2015.
[5]	ZHENG Rongjian, PAN Feng. Prediction of product concentration in glutamate fermentation process using partial least squares and least square support vector machine [J]. CIESC Journal, 2017, 68(3): 976-983.
[6]	SUN Maowei, YANG Huizhong. Gaussian process ensemble soft-sensor modeling based on improved Bagging algorithm [J]. CIESC Journal, 2016, 67(4): 1386-1391.
[7]	SHUANG Yifan, GU Xingsheng. Multi-model soft sensor for hydrogen purity in catalytic reforming process based on improved fast search clustering algorithm and Gaussian processes regression [J]. CIESC Journal, 2016, 67(3): 765-772.
[8]	WANG Jinping, ZHAO Zhonggai, LIU Fei. State estimation approach by incorporating measurements with delay-free and time delay [J]. CIESC Journal, 2016, 67(3): 940-946.
[9]	RONG Gang, YANG Shenglan, ZHOU Peijie, FENG Yiping. A control performance monitoring and assessment system with priority strategy for process industry [J]. CIESC Journal, 2016, 67(3): 707-714.
[10]	LIU Ruilan, XU Yan, RONG Zhou. Modeling soft sensor based on sparse least square support vector machine [J]. CIESC Journal, 2015, 66(4): 1402-1406.
[11]	LI Jun, YUE Wenqi. Dynamic soft sensor modeling and its application using leaky-integrator ESN [J]. CIESC Journal, 2014, 65(10): 4004-4014.
[12]	LEI Yu, YANG Huizhong. Combination model soft sensor based on Gaussian process and Bayesian committee machine [J]. CIESC Journal, 2013, 64(12): 4434-4438.
[13]	. Melt Index Prediction of Polypropylene Based on GMC(1,N) Model [J]. CIESC Journal, 2013, 64(5): 0-0.
[14]	. Melt Index Prediction of Polypropylene Based on GMC(1,N) Model [J]. , 2013, 64(5): 0-0.
[15]	QIAN Xiaoshan, YANG Chunhua, XU Lisha. Soft sensor of sodium aluminate solution concentration based on improved differential evolution algorithm and LSSVM [J]. CIESC Journal, 2013, 64(5): 1704-1709.