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A Modified Version of Canny Filter Applied to Identify Oxidation Points in Steel Plate
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 11 |
| Year of Publication: 2022 |
| Authors: Antonio Eduardo Assis Amorim, Líria Baptista De Rezende, Suzana De Almeida Prado Pohl Sanzovo |
10.5120/ijca2022922091
|
Antonio Eduardo Assis Amorim, Líria Baptista De Rezende, Suzana De Almeida Prado Pohl Sanzovo . A Modified Version of Canny Filter Applied to Identify Oxidation Points in Steel Plate. International Journal of Computer Applications. 184, 11 ( May 2022), 45-52. DOI=10.5120/ijca2022922091
Abstract
Steel plates are present in submerged vessels or civil structures and usually they are in aggressive environments which can initially cause an oxidation process leading to corrosion, which can compromise their physical integrity. Periodically they undergo surveys and depending on the depth or complexity of the structure, these operations are complex and costly. The use of cameras embedded in robotic vehicles allows the use of digital image processing techniques. Oxidation has a peculiar tint which allows the use of edge detection filters. Canny filter has two input parameters, standard deviation and the thresholds that affect the identification of the edges. The purpose in this study is to choose entropy and the threshold of the image and use them as input parameters in the Canny filter to identify the oxidation points. Two images are used, one of them of a piece in which the normalized histogram focuses on dark tones and the other that has a normalized histogram concentrated in light shades. The parameter sigma is a polynomial function of entropy and threshold while the factor is an exponential function of entropy. It is analyzed the situation for both images, one is of a small piece of steel and the other one of a large piece of a ship in RGB and HSV components. Applied to both situations, it is observed that the code can identify the oxidation points of the plate in both light and dark normalized histograms. The results are promising, showing that the filter has a good behavior.
References
- E. J. C. Neves, “Docagem de embarcações – navios graneleiros,” UFRJ, 2019.
- J. E. Modica, “Riscos em Projetos de Docagem de Navios Petroleiros,” USP, 2009.
- L. A. Demsetz and J. Cabrera, “Detection Probability Assessment for Visual Inspection of Ships,” 1999.
- C. Fernández-Isla, P. J. Navarro, and P. M. Alcover, “Automated Visual Inspection of Ship Hull Surfaces Using the Wavelet Transform,” Math. Probl. Eng., vol. 1, pp. 1–12, 2013.
- J. R. de Souza, “Gestão De Projeto Para Docagem De Navios Em Diques,” Universidade Cândido Mendes, 2010.
- J. M. dos S. S. Rodrigues, “Simulador Interactivo de Docagem de Navios em Ambiente Tridimensional,” Instituto Técnico de Lisboa, 2008.
- T. F. da Rocha, “Medidas de proteção à corrosão em cascos de navios militares brasileiros,” Universidade Cândido Mendes, 2011.
- S. Kumar Ahuja, “Surface Corrosion Detection and Classification for Steel Alloy using Image Processing and Machine Learning,” Helix, vol. 8, no. 5, pp. 3822–3827, 2018.
- J. S. Owotogbe, T. S. Ibiyemi, and B. A. Adu, “Edge Detection Techniques on Digital Images - A Review,” Int. J. Innov. Sci. Res. Technol., vol. 4, no. 11, pp. 329–332, 2019.
- P. P. Acharjya, R. Das, and D. Ghoshal, “Study and Comparison of Different Edge Detectors for Image Segmentation,” Glob. J. Comput. Sci. Technol., vol. 12, no. 13, 2012.
- P. H. Buscariolloand A. E. A. Amorim, “Avaliação do desempenho de um sistema de visãoaplicadoaoveículosubmersívelnãotripuladoJaú II,” Fatecnológica, vol. 5, p. 9, 2010.
- R. C. Gonzalez and R. E. Woods, Digital Image Processing. Pearson Education - Prentice Hall, 2018.
- O. Marques, Practical Image and Video Processing Using MATLAB. Hoboken, New Jersey: John Wiley & Sons, 2011.
- S. Gupta, C. Gupta, and S. K. Chakarvarti, “Image Edge Detection: A Review,” Int. J. Adv. Res. Comput. Eng. Technol., vol. 2, no. 7, pp. 2246–2251, 2013.
- A. Çelik et al., “Signal Processing, Image Processing, and Pattern Recognition,” in International Conference, SIP 2009, 2009, vol. 1, no. 1, p. 341.
- G. N. Manjula and M. Ahmed, “A Survey on Various Methods of Edge Detection,” J. Sci. Technol., vol. 1, no. 1, pp. 25–28, 2016.
- S. Vijayarani and A. Sakila, “A Performance Comparison of Edge Detection Techniques for Printed and Handwritten Document Images,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 4, no. 5, pp. 8327–8337, 2016.
- M. Juneja and P. S. Sandhu, “Performance Evaluation of Edge Detection Techniques for Images in Spatial Domain,” Int. J. Comput. Theory Eng., vol. 1, no. 5, pp. 614–621, 2009.
- C. Ghosh, S. Majumder, S. Ray, S. Datta, and S. N. Mandal, “Different EDGE Detection Techniques: A Review,” in Lecture Notes in Electrical Engineering, vol. 686, no. November, 2020, pp. 885–898.
- A. Maan and D. S. Singh, “A Review Paper on Image Segmentation Using Edge Detection Techniques and Threshold in MATLAB,” J. Netw. Commun. Emerg. Technol., vol. 7, no. 6, pp. 30–33, 2017.
- J. S. Y. Sia, T. S. Tan, A. Bin Yahya, M. F. T. Tiong, and J. Y. X. Sia, “Mini Kirsch Edge Detection and Its Sharpening Effect,” Indones. J. Electr. Eng. Informatics, vol. 9, no. 1, pp. 228–244, Mar. 2021.
- A. E. A. Amorim and M. J. P. Cacchi, “Digital Image Processing for Recognition of Imperfections in the Submerged Hull,” Int. J. Innov. Sci. Res. Technol., vol. 5, no. 2, pp. 765–767, 2020.
- A. E. A. Amorim and M. J. P. Cacchi, “Evaluation of Edge Detection Filters Applied to Corroded Steel Sheets,” Int. J. Innov. Sci. Res. Technol., vol. 5, no. 6, pp. 898–902, 2020.
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