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Detection of corrosion on steel structures using automated image processing

Mojtaba Khayatazad (UGent) , Laura De Pue (UGent) and Wim De Waele (UGent)
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Abstract
The traditional method used for corrosion damage assessment is visual inspection which is time-consuming for vast areas, impossible for inaccessible areas and subjective for non-experts. A promising way to overcome the aforementioned drawbacks is to develop an artificial intelligence-based algorithm that can recognize corrosion damage in a series of photographic images. This paper reports on the implementation and use of an algorithm that quantifies and combines two visual aspects – roughness and color – in order to locate the corroded area in a given image. For the roughness analysis, the uniformity metric calculated from the gray-level co-occurrence matrix is considered. For the color analysis, the histogram of corrosion-representative colors extracted from a data-set in HSV color space is used. The algorithm has been applied to a large dataset of photographs of corroded and non-corroded components and structures. Our findings show that the developed algorithm can efficiently locate corroded areas.
Keywords
Steel structures, Corrosion detection, Roughness analysis, Color analysis, HSV color Space

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MLA
Khayatazad, Mojtaba, et al. “Detection of Corrosion on Steel Structures Using Automated Image Processing.” DEVELOPMENTS IN THE BUILT ENVIRONMENT, vol. 3, 2020, doi:10.1016/j.dibe.2020.100022.
APA
Khayatazad, M., De Pue, L., & De Waele, W. (2020). Detection of corrosion on steel structures using automated image processing. DEVELOPMENTS IN THE BUILT ENVIRONMENT, 3. https://doi.org/10.1016/j.dibe.2020.100022
Chicago author-date
Khayatazad, Mojtaba, Laura De Pue, and Wim De Waele. 2020. “Detection of Corrosion on Steel Structures Using Automated Image Processing.” DEVELOPMENTS IN THE BUILT ENVIRONMENT 3. https://doi.org/10.1016/j.dibe.2020.100022.
Chicago author-date (all authors)
Khayatazad, Mojtaba, Laura De Pue, and Wim De Waele. 2020. “Detection of Corrosion on Steel Structures Using Automated Image Processing.” DEVELOPMENTS IN THE BUILT ENVIRONMENT 3. doi:10.1016/j.dibe.2020.100022.
Vancouver
1.
Khayatazad M, De Pue L, De Waele W. Detection of corrosion on steel structures using automated image processing. DEVELOPMENTS IN THE BUILT ENVIRONMENT. 2020;3.
IEEE
[1]
M. Khayatazad, L. De Pue, and W. De Waele, “Detection of corrosion on steel structures using automated image processing,” DEVELOPMENTS IN THE BUILT ENVIRONMENT, vol. 3, 2020.
@article{8672371,
  abstract     = {{The traditional method used for corrosion damage assessment is visual inspection which is time-consuming for vast areas, impossible for inaccessible areas and subjective for non-experts. A promising way to overcome the aforementioned drawbacks is to develop an artificial intelligence-based algorithm that can recognize corrosion damage in a series of photographic images. This paper reports on the implementation and use of an algorithm that quantifies and combines two visual aspects – roughness and color – in order to locate the corroded area in a given image. For the roughness analysis, the uniformity metric calculated from the gray-level co-occurrence matrix is considered. For the color analysis, the histogram of corrosion-representative colors extracted from a data-set in HSV color space is used. The algorithm has been applied to a large dataset of photographs of corroded and non-corroded components and structures. Our findings show that the developed algorithm can efficiently locate corroded areas.}},
  articleno    = {{100022}},
  author       = {{Khayatazad, Mojtaba and De Pue, Laura and De Waele, Wim}},
  issn         = {{2666-1659}},
  journal      = {{DEVELOPMENTS IN THE BUILT ENVIRONMENT}},
  keywords     = {{Steel structures,Corrosion detection,Roughness analysis,Color analysis,HSV color Space}},
  language     = {{eng}},
  pages        = {{12}},
  title        = {{Detection of corrosion on steel structures using automated image processing}},
  url          = {{http://doi.org/10.1016/j.dibe.2020.100022}},
  volume       = {{3}},
  year         = {{2020}},
}

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