Detection of corrosion on steel structures using automated image processing. (August 2020)

• Record Type: Journal Article
• Authors: Khayatazad, M.; De Pue, L.; De Waele, W.
• Is Part Of: Developments in the built environment. Volume 3(2020)
• Publication Date: 2020-08
• Subjects: Steel structures -- Corrosion detection -- Roughness analysis -- Color analysis -- HSV color Space; Civil engineering -- Periodicals; Sustainable construction -- Periodicals; 624.05
• DOI: 10.1016/j.dibe.2020.100022
• Languages: English
• ISSNs: 2666-1659

Abstract

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. Highlights: A numerical framework for screening of uniform corrosion on steel structures. A fully automated, fast and objective screening for corrosion. A database of digital images including rust stains, non-uniform illumination, etc. Image analysis based on the roughness and color analysis, and a performance metric.