Corrosion Severity Index (CSI) for Spectral Characterization of Corroded Steel and Iron Samples

Metals in coastal regions are threatened by corrosion, underscoring the need for precise detection and monitoring methods. Traditional methods often faces limitations in terms of accuracy and applicability under diverse conditions. This study introduces the Corrosion Severity Index (CSI), an innovative spectral index for assessing corrosion in steel and iron structures. Several iron samples were placed in a salt spray chamber to generate different degrees of corrosion. The samples were analysed using hyperspectral cameras covering the visible near-infrared (VNIR) to the shortwave infrared (SWIR) spectrum. A Scale-Invariant Feature Transform (SIFT) registration algorithm was employed to generate the full spectral signatures from 400 nm to 1700 nm for each pixel. The CSI combines four spectral bands (457.50 nm, 791.91 nm, 1305.08 nm, and 1442.60 nm) where a pixel value close to 0 represents the absence of corrosion, whereas a higher value indicates greater severity of corrosion. Based on the average CSI values, samples are classified into Grade A, B, C, or D which indicates the degree of corrosion. CSI demonstrates its ability to detect early-stage corrosion and has been evaluated for robustness across a variety of steel and iron samples in different environmental conditions. In addition, the performance of the CSI is validated by comparing it with the previously published Corrosion Index (CI). CSI demonstrates a higher accurate ability to detect corrosion products and identify the degree of corrosion with a simplified approach. This index allows a balance between accuracy, low computational demands, and usability, providing an optimal solution for early diagnosis and proactive management of corrosion in coastal infrastructures.

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Publication Details

Journal
IEEE Transactions on Instrumentation and Measurement
Published
2025-01-01
DOI
https://doi.org/10.1109/tim.2025.3527548
Citations
4
Primary Topic
Corrosion Behavior and Inhibition
Type
article
Field-Weighted Citation Impact
2.07

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article

Corrosion Severity Index (CSI) for Spectral Characterization of Corroded Steel and Iron Samples

Ámbar Pérez-García, Adrian Rodriguez, Julia Claudia Mirza Rosca, Emma Hernández-Suárez et al.
4 citations
IEEE Transactions on Instrumentation and Measurement
Corrosion Behavior and Inhibition
2.07
article

Corrosion Severity Index (CSI) for Spectral Characterization of Corroded Steel and Iron Samples

Ámbar Pérez-García, Adrian Rodriguez, Julia Claudia Mirza Rosca, Emma Hernández-Suárez, José F. López
article en
4 citations

Abstract

Metals in coastal regions are threatened by corrosion, underscoring the need for precise detection and monitoring methods. Traditional methods often faces limitations in terms of accuracy and applicability under diverse conditions. This study introduces the Corrosion Severity Index (CSI), an innovative spectral index for assessing corrosion in steel and iron structures. Several iron samples were placed in a salt spray chamber to generate different degrees of corrosion. The samples were analysed using hyperspectral cameras covering the visible near-infrared (VNIR) to the shortwave infrared (SWIR) spectrum. A Scale-Invariant Feature Transform (SIFT) registration algorithm was employed to generate the full spectral signatures from 400 nm to 1700 nm for each pixel. The CSI combines four spectral bands (457.50 nm, 791.91 nm, 1305.08 nm, and 1442.60 nm) where a pixel value close to 0 represents the absence of corrosion, whereas a higher value indicates greater severity of corrosion. Based on the average CSI values, samples are classified into Grade A, B, C, or D which indicates the degree of corrosion. CSI demonstrates its ability to detect early-stage corrosion and has been evaluated for robustness across a variety of steel and iron samples in different environmental conditions. In addition, the performance of the CSI is validated by comparing it with the previously published Corrosion Index (CI). CSI demonstrates a higher accurate ability to detect corrosion products and identify the degree of corrosion with a simplified approach. This index allows a balance between accuracy, low computational demands, and usability, providing an optimal solution for early diagnosis and proactive management of corrosion in coastal infrastructures.

IEEE Transactions on Instrumentation and MeasurementVol. 74
Universidad de Las Palmas de Gran Canaria (ES), Transylvania University of Brașov (RO)
European Commission, Universidad de Las Palmas de Gran Canaria, Agencia Canaria de Investigación, Innovación y Sociedad de la Información
Life below water
Openalex Percentile: Top 15%
Corrosion Behavior and Inhibition
2.07
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