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Corrosion Modelling Using Convolutional Neural Networks: A Brief Overview

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Abstract

Convolutional Neural Network (CNN) is a type of artificial neural network which is trained using image data. This network architecture consists of a convolutional base and a dense head which helps in classification tasks. This trained networks is then used to solve complex problems like determining the magnitude of damage caused by corrosion. Typically the design of a CNN model would involve image collection, pre-processing, feature extraction and analysis. This paper presents a brief overview of various applications of CNN-based models to corrosion in selected industries. The use of transfer learning to build corrosion CNN models is also discussed. When they are combined with recursive algorithms, the application of CNN models to pinpoint exact locations where corrosion occurs is discussed. From the works reviewed, CNN models can be applied when limited data are available using the freeze transfer learning approach. Convolutional neural networks have shown promising applications for corrosion classification purposes with accuracies above 80%.

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Ibadan, P.O. Box 22133, Oyo State, Nigeria

    Nosa Idusuyi, Oluwatosin Joshua Samuel, Temilola Taiwo Olugasa, Olusegun Olufemi Ajide & Rahaman Abu

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  1. Nosa Idusuyi
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  2. Oluwatosin Joshua Samuel
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Idusuyi, N., Samuel, O.J., Olugasa, T.T. et al. Corrosion Modelling Using Convolutional Neural Networks: A Brief Overview. J Bio Tribo Corros 8, 72 (2022). https://doi.org/10.1007/s40735-022-00671-3

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