Detection of Corrosion-Indicating Oxidation Product Colors in Steel Bridges under Varying Illuminations, Shadows, and Wetting Conditions

Dayakar L Naik,Genda Chen,Ravi Kiran,Hizb Ullah Sajid

doi:10.3390/met10111439

Abstract

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.

Highlights

Corrosion damage is found to play a vital role in the overall maintenance cost of the steel structures [1,2,3]
Sixteen combinations of multi-layer perceptron (MLP) configurations and color spaces are assessed for determining the best combination i.e., the capability of each combinationand to predict the class labels
Color spaces in conjunction with different MLP configurations are explored to detect corrosion initiation in steel structures under ambient lighting conditions

Summary

Introduction

Corrosion damage is found to play a vital role in the overall maintenance cost of the steel structures [1,2,3]. In the United States, the average annual cost of corrosion damage for steel bridges is estimated to be ~$10.15 billion [3]. Detection of corrosion in its early stages results in the reduction of maintenance costs and increases the life of the structures [4]. Digital images of structures are first acquired on-site and are analyzed using image processing techniques off-site to detect the corrosion. Various approaches have been proposed by researchers to detect the corrosion in steel structures using digital images [14,15,16]

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Results

Conclusion