Corrosion Area Identification of Sheet Metal Based on K-means++ Clustering Algorithm and SSIM Index

doi:10.11902/1005.4537.2023.112

Journal of Chinese Society for Corrosion and protection

2024, Vol. 44

Issue (2): 437-444 DOI: 10.11902/1005.4537.2023.112

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Corrosion Area Identification of Sheet Metal Based on K-means++ Clustering Algorithm and SSIM Index

LONG Mengxiang, FU Guicui, WAN Bo(

), ZHANG Zhongqing

School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China

Cite this article:

LONG Mengxiang, FU Guicui, WAN Bo, ZHANG Zhongqing. Corrosion Area Identification of Sheet Metal Based on K-means++ Clustering Algorithm and SSIM Index. Journal of Chinese Society for Corrosion and protection, 2024, 44(2): 437-444.

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Abstract

Various corrosion characteristics such as rust spots and cracks will appear on the surface of metal plates after corrosion. Their corrosion degree can be determined by corrosion characteristics. At present, the corrosion degree of metal plates is mainly judged by manual visual inspection. But it has many non-ignorable shortcomings such as low consistency and low efficiency etc. In this paper, the RGB values of image pixels of corroded metal sheets were collected and then clustered by means of K-means++ clustering algorithm, afterwards the relevant corroded- and uncorroded-regions were separated. Whether corrosion occurred or not was judged in each cluster area by means of image structural similarity index SSIM. The results show that setting the number of clustering centers ‘k’ to 5 can effectively delineate each clustering area based on the image color distribution. Compared to peak signal-to-noise ratio and mean square error, the structural similarity index SSIM is strongly correlated with the occurrence of erosion. Setting the SSIM index threshold at 0.95 can effectively judge whether erosion occurred in each cluster area. Compared to manually dividing corrosion areas based on pixel color, our method had a higher identification efficiency and an accuracy of not less than 90%. This research can be applied to automate the evaluation of the corrosion degree of metal sheets after environmental testing.

Key words: corrosion evaluation image processing K-means++ algorithm clustering image similarity intelligent diagnosis corrosion area

Received: 14 April 2023 32134.14.1005.4537.2023.112

ZTFLH:

TP391.41

Corresponding Authors: WAN Bo, E-mail: wanbo@buaa.edu.cn

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https://www.jcscp.org/EN/10.11902/1005.4537.2023.112 OR https://www.jcscp.org/EN/Y2024/V44/I2/437

Fig.1 Flow chart of K-means clustering algorithm

Fig.2 Flow chart for determining initial cluster center of K-means++ clustering algorithm

Fig.3 Surface images of the metal sheet before (a) and after (b) corrosion

Fig.4 Images of cluster areas 1 (a), 2 (b), 3 (c), 4 (d) and 5 (e)

Fig.5 Images of the cluster area 1 of the metal sheet before (a) and after (b) corrosion

Table 1 Calculation results of image similarity index of clustering area

Fig.6 Corrosion area image recognition results of the metal sheet before (a) and after (b) corrosion

Fig.7 Raw images (a1-d1), image recognition results (a2-d2) and manual “number grid” recognition results (a3-d3) of the corrosion area of the metal sheet

1	Cao X L. Investigation of atmospheric corrosivity with ACM technology[D]. Wuhan: Academy of Machinery Science & Technology, 2005
	曹献龙. ACM技术研究大气环境腐蚀严酷性[D]. 武汉: 机械科学研究院, 2005
2	Xu D, Yang X J, Li Q, et al. Review on corrosion test methods and evaluation techniques for materials in atmospheric environment[J]. J. Chin. Soc. Corros. Prot., 2022, 42: 447
	徐迪, 杨小佳, 李清等. 材料大气环境腐蚀试验方法与评价技术进展[J]. 中国腐蚀与防护学报, 2022, 42: 447
3	Uhlig H H. The cost of corrosion to the United States[J]. Corrosion, 1950, 6: 29 doi: 10.5006/0010-9312-6.1.29
4	Fei Y W, Peng B, Xiao Y P. Review on the development of digital processing technology for metal corrosion images[J]. Dig. Technol. Appl., 2010, (11): 45
	费逸伟, 彭彬, 校云鹏. 金属腐蚀图像数字处理技术发展综述[J]. 数字技术与应用, 2010, (11): 45
5	Wang M H, Wang H, Liu R, et al. Research on image recognition for NiCrAlY Coating/N5 high-temperature alloy system based on deep learning method[J]. J. Chin. Soc. Corros. Prot., 2022, 42: 583
	王明好, 王欢, 刘叡等. 基于深度学习方法的N5/NiCrAlY涂层图像识别的研究[J]. 中国腐蚀与防护学报, 2022, 42: 583 doi: 10.11902/1005.4537.2021.234
6	Tan C, Sun Y, Li G F, et al. Image segmentation technology based on genetic algorithm[A]. Proceedings of the 2019 3rd International Conference on Digital Signal Processing[C]. Jeju Island, Republic of Korea, 2019: 27
7	Lei F, Xiong J B, Zhang L, et al. Image enhancement algorithm in metal corrosion area[J]. CAAI Trans. Intell. Syst., 2019, 14: 385
	雷芳, 熊建斌, 张磊等. 金属腐蚀区域图像增强算法研究[J]. 智能系统学报, 2019, 14: 385
8	Li Z H, Zhang L, Wang J M, et al. Research on image acquisition and preprocessing method for visual measurement of pipeline inner surface corrosion[A]. 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)[C]. Yangzhou, China, 2017: 364
9	Zhu Z T, Mu Z T, Su W G, et al. Corrosion grade evaluation of aluminum alloy based on image processing technique[J]. J. Nanjing Univ. Aeronaut. Astronaut., 2010, 42: 383
	朱做涛, 穆志韬, 苏维国等. 基于图像处理技术的铝合金腐蚀等级评定方法[J]. 南京航空航天大学学报, 2010, 42: 383
10	Xu J, Luan Y H. Application of digital image processing technology in corrosion science[J]. Total Corros. Control, 2015, 29(1): 43
	徐健, 栾艳华. 数字图像处理技术在腐蚀科学中的应用研究[J]. 全面腐蚀控制, 2015, 29(1): 43
11	Nan C H. Research on corrosion mechanism and damage evaluation of ocean engineering materials based on image recognition[D]. Xi'an: Xi'an University of Technology, 2021
	南翠红. 基于图像识别的海洋工程材料腐蚀机理及损伤评价研究[D]. 西安: 西安理工大学, 2021
12	Huang X B, Zhang X L, Zhang Y, et al. A method of identifying rust status of dampers based on image processing[J]. IEEE Trans. Instrum. Meas., 2020, 69: 5407 doi: 10.1109/TIM.19
13	Petricca L, Moss T, Figueroa G, et al. Corrosion detection using A. I: a comparison of standard computer vision techniques and deep learning model[A]. The Sixth International Conference on Computer Science, Engineering and Information Technology[C]. Viena, Austria, 2016
14	Zhou G Y, Zhang Z J, Yin W L, et al. Surface defect detection of CFRP materials based on infrared thermography and Attention U-Net algorithm[J]. Nondestruct. Test. Eval., doi: 10.1080/10589759.2023.2191954
15	Ibrahim A, Dalbah A, Abualsaud A, et al. Application of machine learning to evaluate insulator surface erosion[J]. IEEE Trans. Instrum. Meas., 2020, 69: 314 doi: 10.1109/TIM.19
16	Wang S S, Ma S J, Che K, et al. Application status of machine learning in field of natural environment corrosion assessment and prediction[J]. J. Chin. Soc. Corros. Prot., 2023, 43: 441
	王莎莎, 马帅杰, 车琨等. 机器学习在自然环境腐蚀评估与预测领域的应用现状[J]. 中国腐蚀与防护学报, 2023, 43: 441 doi: 10.11902/1005.4537.2022.147
17	Rahman A, Wu Z Y, Kalfarisi R. Semantic deep learning integrated with RGB feature-based rule optimization for facility surface corrosion detection and evaluation[J]. J. Comput. Civil Eng., 2021, 35: 4021018 doi: 10.1061/(ASCE)CP.1943-5487.0000982
18	MacQueen J. Classification and analysis of multivariate observations[A]. 5th Berkeley Symposium on Mathematical Statistics and Probability[C]. Los Angeles, USA, 1967: 281
19	Liu Y. Laryngoscope image classification by an improved convolutional neural network based on entropy weighted K-means clustering[D]. Changchun: Changchun University of Technology, 2022
	刘阳. 基于加权熵K-means聚类的改进卷积神经网络下喉镜图像分类研究[D]. 长春: 长春工业大学, 2022
20	Zhong J, Lin C, Sheng Z W, et al. Quantum K-means algorithm based on Hamming distance[J]. J. Comput. Appl., 2023, 43(8): 2493
	钟静, 林晨, 盛志伟等. 基于汉明距离的量子K-Means算法[J]. 计算机应用, 2023, 43(8): 2493
21	Arthur D, Vassilvitskii S. K-means++: The advantages of careful seeding[A]. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms[C]. New Orleans, USA, 2006
22	Thorndike R L. Who belongs in the family?[J]. Psychometrika, 1953, 18: 267 doi: 10.1007/BF02289263
23	Rousseeuw P J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis[J]. J. Comput. Appl. Math., 1987, 20: 53
24	Tibshirani R, Walther G, Hastie T. Estimating the number of clusters in a data set via the gap statistic[J]. J. R. Stat. Soc., 2001, 63B: 411
25	Sugar C A, James G M. Finding the number of clusters in a dataset: An information-theoretic approach[J]. J. Am. Stat. Assoc., 2003, 98: 750 doi: 10.1198/016214503000000666
26	Hirsch R. Exploring Colour Photography: A Complete Guide[M]. Berlin: Laurence King Publishing, 2005
27	Wang Z, Bovik A C. Mean squared error: Love it or leave it? A new look at signal fidelity measures[J]. IEEE Signal Process. Mag., 2009, 26: 98
28	Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Trans. Image Process., 2004, 13: 600 doi: 10.1109/TIP.2003.819861

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