Application of Semi-Supervised Clustering Algorithm Based on Color Feature to Corrosion Level Identification for Copper

QIAN Qihao; ZHENG Zhanguang; LIANG Zhao; WU Pengge; DU Pengyu

doi:10.11973/fsyfh-202305007

Corrosion & Protection > 2023 > 44(5): 34-40. > DOI: 10.11973/fsyfh-202305007

QIAN Qihao, ZHENG Zhanguang, LIANG Zhao, WU Pengge, DU Pengyu. Application of Semi-Supervised Clustering Algorithm Based on Color Feature to Corrosion Level Identification for Copper[J]. Corrosion & Protection, 2023, 44(5): 34-40. DOI: 10.11973/fsyfh-202305007

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Application of Semi-Supervised Clustering Algorithm Based on Color Feature to Corrosion Level Identification for Copper

1. School of Mechanical Engineering, Guangxi University, Nanning 530004, China;
2. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China

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Received Date: May 26, 2021

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Abstract

A rapid identification method of copper corrosion level based on semi-supervised clustering algorithm was proposed. In this method, a larger number of images of corroded copper were segmented firstly in order to normalize their sizes. Then the influence of outliers was weakened by filter processing, and the color feature vector of the images was obtained by color quantization. The dimension of color histogram was reduced by the kernel principal component analysis (KPCA) method. Finally, the corresponding corrosion level of each image was obtained by taking the color feature vector extracted from the standard colorimetric card as the initial clustering center of semi-supervised k-means in combination with the color feature vector training model of pre-processed corrosion images. The results showed that classification results of corrosion level of copper by calculation through the algorithm corresponded well to the visual inspection results, indicating high accuracy of the method.
- copper corrosion,
- color feature,
- image preprocessing,
- semi-supervised clustering,
- kernal principal component analysis (KPCA)

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[1]	HEIM A. Corrosion resistance of copper and copper alloys[J]. Library Management, 1957, 49(8):63-66.
[2]	VERNON J. Oxidation, corrosion and other effects[M]. London:Palgrave Macmillan, 1992.
[3]	WANG J L, ZHANG T S, ZHANG X X. Inhibition effects of benzalkonium chloride on chlorella vulgaris induced corrosion of carbon steel[J]. Journal of Materials Science & Technology, 2020(8):14-20.
[4]	BUTUSOVA Y, MISHAKIN V, KACHANOV M. On monitoring the incubation stage of stress corrosion cracking in steel by the eddy current method[J]. International Journal of Engineering Science, 2020, 148:103212.
[5]	SHAH J K, BRAGA H B F, MUKHERJEE A, et al. Ultrasonic monitoring of corroding bolted joints[J]. Engineering Failure Analysis, 2019, 102:7-19.
[6]	WU K, BYEON J. Morphological estimation of pitting corrosion on vertically positioned 304 stainless steel using acoustic-emission duration parameter[J]. Corrosion Science, 2019, 148:331-337.
[7]	JAMSHIDI V, DAVARNEJAD R. Simulation of corrosion detection inside wellbore by X-ray backscatter radiography[J]. Applied Radiation and Isotopes:Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine, 2019, 145:116-119.
[8]	LIU S H, LIU Y L, ZHONG H, et al. Experimental study on corrosion resistance of coiled tubing welds in high temperature and pressure environment[J]. PLoS One, 2021, 16(1):0244237.
[9]	ADELOJU S B. X-ray diffraction analysis of corrosion products on electrochemically polarized copper surface[J]. Microchimica Acta, 1985, 87(5):401-415.
[10]	BEN CHANNOUF R, SOUISSI N, ZANNA S, et al. Surface characterization of the corrosion product layer formed on synthetic bronze in aqueous chloride solution and the effect of the adding of juniperus communis extract by X-ray photoelectron spectroscopy analysis[J]. Chemistry Africa, 2018, 1(3):167-174.
[11]	BUSCHOW K H J. Encyclopedia of Materials:Science and Technology[M]. Amsterdam:Elsevier, 2001.
[12]	ZUMELZU E, CABEZAS C, VERA A. Scanning electron microscopy analysis of corrosion degradation on tinplate substrates[J]. Scanning, 2003, 25(1):34-36.
[13]	WICKER M, ALDUSE B P, JUNG S. Detection of hidden corrosion in metal roofing shingles utilizing infrared thermography[J]. Journal of Building Engineering, 2018, 20:201-207.
[14]	杜爱玲, 侯文涛, 张鹤鸣, 等. 线性极化方法测量混凝土中钢筋的腐蚀速度[J]. 电化学, 2000, 6(3):297-304.
[15]	XIA D H, MA C, SONG S Z, et al. Erratum:detection of atmospheric corrosion of aluminum alloys by electrochemical probes:theoretical analysis and experimental tests[J]. Journal of The Electrochemical Society, 2019, 166(12):B1000-B1009.
[16]	MONRRABAL G, HUET F, BAUTISTA A. Electrochemical noise measurements on stainless steel using a gelled electrolyte[J]. Corrosion Science, 2018, 148:48-56.
[17]	WONG A, LOU S L. Medical image archive, retrieval, and communication[M]//Handbook of Medical Imaging. Amsterdam:Elsevier, 2000:771-781.
[18]	CIORA R A, SIMION C M. Industrial applications of image processing[J]. Acta Universitatis Cibiniensis Technical Series, 2014, 64(1):17-21.
[19]	JENSEN J R. Introductory Digital Image Processing:a Remote Sensing Perspective[M]. Englewood Cliffs, NJ:Prentice-Hall, 1986.
[20]	VIBHUTE A, BODHE S. Applications of image processing in agriculture:a survey[J]. International Journal of Computer Applications, 2012, 52(2):34-40.
[21]	TYAGI V. Understanding Digital Image Processing[M]. Boca Raton, FL:Taylor & Francis Group, 2018.
[22]	SHINDE B. The origins of digital image processing & application areas in digital image processing medical images[J]. IOSR Journal of Engineering, 2011, 1(1):66-71.
[23]	KHAYATAZAD M, DE PUE L, DE WAELE W. Detection of corrosion on steel structures using automated image processing[J]. Developments in the Built Environment, 2020, 3:100022.
[24]	方叶祥, 甘平, 陈俐. 金属表面缺陷检测的改进YOLOv3算法研究[J]. 机械科学与技术, 2020, 39(9):1390-1394.
[25]	龚应忠, 李子存, 冯新泸, 等. 基于颜色特征的铜片腐蚀结果评价[J]. 腐蚀与防护, 2013, 34(2):117-120, 150.
[26]	陈桂娟, 贾春雨, 邹龙庆, 等. 基于腐蚀图像与支持向量机的CO₂腐蚀类型识别方法研究[J]. 化工机械, 2014, 41(6)742-745.
[27]	STOEAN R, STOEAN C, SAMIDE A. Deep learning for metal corrosion control:can convolutional neural networks measure inhibitor efficiency[C]//201820th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC).Timisoara, Romania:IEEE, 2019:387-393.
[28]	陈爽. 基于深度神经网络的耐候桥梁钢锈蚀图像识别[D]. 北京:北京交通大学.
[29]	MOHAMED F A H, FRIEDHELM S. Semi-supervised learning[J]. Journal of the Royal Statistical Society, 2006, 172(2):530-530.
[30]	王海峰, 管亮. 基于颜色特征的图像分类技术在油品分析中的应用[J]. 仪器仪表学报, 2004, 25(S3):348-350.
[31]	田玉敏, 梁若莹. 计算机彩色输入输出设备常用颜色空间及其转换[J]. 计算机工程, 2002, 28(9)198-200, 274.
[32]	王正家, 解家月, 柯黎明, 等. 一种结合彩色图像分割的图像匹配算法[J]. 机械科学与技术, 2020, 39(9)1419-1425.
[33]	SERGIO M S, EDGARDO M F R, LUIS P S F. A Simple and effective method of color image quantization[C]//Progress in Pattern Recognition, Image Analysis and Applications. Berlin:Springer-Verlag, 2008.
[34]	NEMA S, SAHAR H. Data dimensional reduction and principal components analysis[J]. Procedia Computer Science, 2019, 163:292-299.
[35]	HU X, XIAO Z, LIU D, et al. KPCA and AE based local-global feature extraction method for vibration signals of rotating machinery[J]. Mathematical Problems in Engineering, 2020, 2020:5804509.
[36]	MORISSETTE L, CHARTIER S. The k-means clustering technique:general considerations and implementation in mathematica[J]. Tutorials in Quantitative Methods for Psychology, 2013, 9(1):15-24.
[37]	DEZA M M, DEZA E. Encyclopedia of distances[M]. 2nd ed. Berlin:Springer-Verlag, 2013.
[38]	杜彭玉. 防锈润滑油高温腐蚀抑制及防锈性能研究[D]. 长沙:湖南大学.
[39]	BIBIKOV S A, FURSOV V A, NIKONOROV A V. Boundaries detection and color correction of shadows in color images[J]. Pattern Recognition and Image Analysis, 2011, 21(1):3-8.
[40]	BOYAT A K, JOSHI B K. A review paper:noise models in digital image processing[EB/OL]. 2015:arXiv:1505.03489. https://arxiv.org/abs/1505.03489.
[41]	BANKMAN I N. Handbook of medical image processing and analysis[M]. 2nd ed. Amsterdam:Elsevier/Academic Press, 2009.

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