Early detection of corrosion in steel bridges is essential for strategizing the mitigation of further corrosion damage. Although various image-based approaches are available in the literature for corrosion detection, most of these approaches are tested on images acquired under uniform natural daylight illuminations i.e., inherent variations in the ambient lighting conditions are ignored. Owing to the fact that varying natural daylight illuminations, shadows, water wetting, and oil wetting are unavoidable in real-world scenarios, it is important to devise a robust technique for corrosion identification. In the current study, four different color spaces namely ‘RGB’, ‘rgb’, ‘HSV’ and ‘CIE La*b*’ along with a multi-layer perceptron (MLP) is configured and trained for detecting corrosion under above-mentioned real-world illumination scenarios. Training (5000 instances) and validation (2064 instances) datasets for this purpose are generated from the images of corroded steel plates acquired in the laboratory under varying illuminations and shadows, respectively. Each combination of color space and an MLP configuration is individually assessed and the best suitable combination that yields the highest ‘Recall’ value is determined. An MLP configuration with a single hidden layer consisting of 4 neurons (1st Hidden Layer (HL)(4N)) in conjunction with ‘rgb’ color space is found to yield the highest ‘Accuracy’ and ‘Recall’ (up to 91% and 82% respectively). The efficacy of the trained MLP to detect corrosion is then demonstrated on the test image database consisting of both lab-generated partially corroded steel plate images and field-generated images of a bridge located in Moorhead (Minnesota). Lab-generated images used for testing are acquired under varying illuminations, shadows, water wetting, and oil wetting conditions. Based on the validation studies, ‘rgb’ color space and an MLP configuration consisting of single hidden layer with 4 neurons (1st HL(4N)) trained on lab-generated corroded plate images identified corrosion in the steel bridge under ambient lighting conditions.
D. L. Naik et al., "Detection of Corrosion-Indicating Oxidation Product Colors in Steel Bridges under Varying Illuminations, Shadows, and Wetting Conditions," Metals, vol. 10, no. 11, pp. 1-19, MDPI, Nov 2020.
The definitive version is available at https://doi.org/10.3390/met10111439
Civil, Architectural and Environmental Engineering
INSPIRE - University Transportation Center
Keywords and Phrases
Artificial neural networks; Casted shadows; Color spaces; Corroded steel bridges; Corrosion detection; Varying illumination
International Standard Serial Number (ISSN)
Article - Journal
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01 Nov 2020