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http://dx.doi.org/10.5695/JSSE.2022.55.3.164

Corrosion image analysis on galvanized steel by using superpixel DBSCAN clustering algorithm  

Kim, Beomsoo (Department of Mechanical System Engineering, Gyeongsang National University)
Kim, Yeonwon (Division of Mechatronics Engineering, Mokpo National Maritime University)
Lee, Kyunghwang (Steel Solution R&D Center, POSCO)
Yang, Jeonghyeon (Department of Mechanical System Engineering, Gyeongsang National University)
Publication Information
Journal of the Korean institute of surface engineering / v.55, no.3, 2022 , pp. 164-172 More about this Journal
Abstract
Hot-dip galvanized steel(GI) is widely used throughout the industry as a corrosion resistance material. Corrosion of steel is a common phenomenon that results in the gradual degradation under various environmental conditions. Corrosion monitoring is to track the degradation progress for a long time. Corrosion on steel plate appears as discoloration and any irregularities on the surface. This study developed a quantitative evaluation method of the rust formed on GI steel plate using a superpixel-based DBSCAN clustering method and k-means clustering from the corroded area in a given image. The superpixel-based DBSCAN clustering method decrease computational costs, reaching automatic segmentation. The image color of the rusty surface was analyzed quantitatively based on HSV(Hue, Saturation, Value) color space. In addition, two segmentation methods are compared for the particular spatial region using their histograms.
Keywords
Corrosion; Superpixel; DBSCAN; k-means clustering; HSV color space;
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1 M. H. Hong, D. G. Kang, D. J. Paik, H. S. Hwang, S. H. Park, Effect of added magneisum on the coating properties of galvanized steel sheets, Korean J. Met. Mater., 54 (2016) 723-731.   DOI
2 V. Bondada, D. K. Pratihar, C. S. Kumar, Detection and quantitative assessment of corrosion on pipelines through image analysis, Procedia Comput. Sci., 133 (2018) 804-811.   DOI
3 C. Rother, V. Kolmogorov, A. Blake, "GrabCut":interactive foreground extraction using iterated graph cuts, ACM Trans. Graph., 23 (2004) 309-314.   DOI
4 R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Susstrunk, SLIC superpixels compared to state-of-the-art superpixel methods, IEEE Trans. Pattern Anal. Mach. Intell., 34, (2012) 2274-2282.   DOI
5 R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Susstrunk, SLIC superpixels compared to state-of-the-art superpixel methods, IEEE Trans. Pattern Anal. Mach. Intell., 34 (2012) 2274-2282.   DOI
6 S. J. Kwon, S. M. Lee, M. H. Lee, S. S. Park, Study on corrosion and structural performancein hot-dip galvanizing steel, J. Korea Concr. Inst., 24 (2012) 613-621.   DOI
7 B. S. Kim, Y. W. Kim, J. H. Yang, Detection of corrosion on steel plate by using image segmentation method, J. Korean Inst. Surf. Eng., 54 (2021) 84-89.   DOI
8 K. W. Liao, Y. T. Lee, Detection of rust defects on steel bridge coatings via digital image recognition, Autom. Constr., 71 (2016) 294-306.   DOI
9 B. S. Kim, J. S. Kwon, S. W. Choi, J. P. Noh, K. H. Lee, J. H. Yang, Corrosion image monitoring of steel plate by using k-means clustering, J. Korean Inst. Surf. Eng., 54 (2021) 278-284.   DOI
10 K. P. Sinaga, M. S. Yang, Unsupervised k-means clustering algorithm, IEEE Access, 8 (2020) 80716-80727.   DOI
11 D. M. Saputra, D. Saputra, L.D. Oswari, Effect of distance metrics in determining k-value in k-means clustering using elbow and silhouette method, Adv. Intell. Syst. Res., 172 (2019) 341-346.
12 M. Marzeh, M. Tahmasbi, N. Mirehi, Algorithm for finding the largest inscribed rectangle in polygon, J. Algorithm. Comput. Technol., 51 (2019) 29-41.
13 E. Schubert, J. Sander, M. Ester, H. P. Kriegel, X. Xu, DBSCAN revisited, revisited: why and how you should (still) use DBSCAN, ACM Trans. Database Syst. (TODS), 42 (2017) 1-21.
14 M. Ester, H. P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD 96). AAAI Press. (1996) 226-231.
15 M. Ahmed, R. Seraj, S. M. S. Islam, The k-means algorithm: a comprehensive survey and performance evaluation, Electronics, 9 (2020) 1295.   DOI
16 Y. Zhang, Q. Guo, Y. Zhang, C. Zhang, Fast and robust superpixel generation method, IET Image Process., 14 (2020) 4543-4553.   DOI