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.


Civil, Architectural and Environmental Engineering

Research Center/Lab(s)

INSPIRE - University Transportation Center


The authors gratefully acknowledge the financial support from North Dakota Established Program to Stimulate Competitive Research (ND EPSCoR).

Keywords and Phrases

Artificial neural networks; Casted shadows; Color spaces; Corroded steel bridges; Corrosion detection; Varying illumination

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Document Type

Article - Journal

Document Version

Final Version

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© 2020 The Authors, All rights reserved.

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Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

01 Nov 2020