A Deep Learning Image Corrosion Classification Method for Marine Vessels Using an Eigen Tree Hierarchy Module

Chliveros, Georgios; Tzanetatos, Iason; Kontomaris, Stylianos V.

doi:10.3390/coatings14060768

Open AccessEditor’s ChoiceArticle

A Deep Learning Image Corrosion Classification Method for Marine Vessels Using an Eigen Tree Hierarchy Module

by

Georgios Chliveros

^1,*

,

Iason Tzanetatos

²

and

Stylianos V. Kontomaris

¹

Department of Engineering, Metropolitan College, Marousi Campus, 15125 Athens, Greece

²

Core Innovation Center, Core Innovation and Technology OE, 17343 Athens, Greece

^*

Author to whom correspondence should be addressed.

Coatings 2024, 14(6), 768; https://doi.org/10.3390/coatings14060768

Submission received: 30 May 2024 / Revised: 13 June 2024 / Accepted: 14 June 2024 / Published: 18 June 2024

(This article belongs to the Special Issue Effects of Surface Layer Modification on Fatigue, Corrosion and Wear Behavior of Metallic Materials)

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Abstract

This paper involves the automation of a visual characterisation technique for corrosion in marine vessels, as it appears in the hull preventive coatings of marine vessels and their surfaces. We propose a module that maximizes the utilisation of features learned by a deep convolutional neural network to identify areas of corrosion and segment pixels in regions of inspection interest for corrosion detection. Our segmentation module is based on Eigen tree decomposition and information-based decision criteria in order to produce specific corroded spots—regions of interest. To assess performance and compare it with our method, we utilize several state-of-the-art deep learning architectures.The results indicate that our method achieves higher accuracy and precision while maintaining the significance score across the entire dataset. To the best of our knowledge, this is the first Eigen tree-based module in the literature in the context of trained neural network predictors for classifying corrosion in marine vessel images.

Keywords: marine corrosion; corrosion detection; preventive monitoring; image segmentation; deep learning

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MDPI and ACS Style

Chliveros, G.; Tzanetatos, I.; Kontomaris, S.V. A Deep Learning Image Corrosion Classification Method for Marine Vessels Using an Eigen Tree Hierarchy Module. Coatings 2024, 14, 768. https://doi.org/10.3390/coatings14060768

AMA Style

Chliveros G, Tzanetatos I, Kontomaris SV. A Deep Learning Image Corrosion Classification Method for Marine Vessels Using an Eigen Tree Hierarchy Module. Coatings. 2024; 14(6):768. https://doi.org/10.3390/coatings14060768

Chicago/Turabian Style

Chliveros, Georgios, Iason Tzanetatos, and Stylianos V. Kontomaris. 2024. "A Deep Learning Image Corrosion Classification Method for Marine Vessels Using an Eigen Tree Hierarchy Module" Coatings 14, no. 6: 768. https://doi.org/10.3390/coatings14060768

APA Style

Chliveros, G., Tzanetatos, I., & Kontomaris, S. V. (2024). A Deep Learning Image Corrosion Classification Method for Marine Vessels Using an Eigen Tree Hierarchy Module. Coatings, 14(6), 768. https://doi.org/10.3390/coatings14060768

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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A Deep Learning Image Corrosion Classification Method for Marine Vessels Using an Eigen Tree Hierarchy Module

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