Detection of corrosion on steel structures using automated image processing
- Author
- Mojtaba Khayatazad (UGent) , Laura De Pue (UGent) and Wim De Waele (UGent)
- Organization
- Project
- 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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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8672371
- 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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