Metal loss defect detection and depth estimation using multi-spectral image analysis of cooling excited steel specimen with corrosion

Metal loss defect detection and depth estimation using multi-spectral image analysis of cooling excited steel specimen with corrosion

Abstract Imaging techniques have considerably improved corrosion-induced metal loss defect detection and severity estimation in recent decades. Even though the detection of defects using imaging techniques in steel is well established, determining the severity remains difficult due to the necessity...

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Bibliographic Details
Main Authors: Shamendra Egodawela, Amirali K. Gostar, H. A. D. Samith Buddika, W. A. N. I. Harischandra, A. J. Dhammika, Mojtaba Mahmoodian
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Non-destructive testing
Corrosion severity estimation
Multi-spectral imaging
Machine learning
Online Access:https://doi.org/10.1038/s41598-025-88528-w
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https://doi.org/10.1038/s41598-025-88528-w

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