基于颜色特征的半监督聚类算法在铜片腐蚀等级识别中的应用
Application of Semi-Supervised Clustering Algorithm Based on Color Feature to Corrosion Level Identification for Copper
摘 要
提出一种基于半监督聚类算法的铜片腐蚀等级快速识别方法。该方法首先对于大量铜片腐蚀图像进行图像分割，使其尺寸归一化；然后通过滤波处理减弱异常值影响，利用颜色量化方法获取图像的颜色特征向量，并通过核主成分分析（KPCA）对颜色直方图信息进行降维处理；最后，将标准比色卡提取的颜色特征向量作为半监督k-means的初始聚类中心，结合预处理后腐蚀图像的颜色特征向量训练模型，得到每张图片对应的腐蚀等级。结果表明，通过该算法得到的铜片腐蚀等级分类结果与目测结果一致，说明该方法具有较高的准确性。
颜色特征
图像预处理
半监督聚类
核主成分分析
copper corrosion
color feature
image preprocessing
semi-supervised clustering
kernal principal component analysis (KPCA)
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.
中图分类号 TG174 DOI 10.11973/fsyfh-202305007
所属栏目 数值模拟
基金项目 国家自然科学基金项目（51675110，51465002）
收稿日期 2021/5/27
修改稿日期
网络出版日期
作者单位点击查看
引用该论文: 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
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【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.
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【12】ZUMELZU E, CABEZAS C, VERA A. Scanning electron microscopy analysis of corrosion degradation on tinplate substrates[J]. Scanning, 2003, 25(1):34-36.
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【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.
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【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.
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【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.
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【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.
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