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Coating Application and Corrosion Protection
ArticleName Identification and localization of pitting corrosion on metallic surface using deep learning
DOI 10.17580/cisisr.2024.01.15
ArticleAuthor N. V. Krysko, N. A. Shchipakov, D. M. Kozlov, A. G. Kusyy

Bauman Moscow State Technical University (Moscow, Russia)

N. V. Krysko, Cand. Eng., Associate Prof., Dept. MT-7 “Welding Tehnology and Diagnostics”, e-mail:
N. A. Shchipakov, Cand. Eng., Associate Prof., Dept. MT-7 “Welding Tehnology and Diagnostics”, e-mail:
D. M. Kozlov, Cand. Eng., Engineer, e-mail:
A. G. Kusyy, Engineer, e-mail:


In this work, a computer vision system is proposed, which allows the identification and localization of pitting corrosion on metallic surface of the gas pipelines made of low carbon and low alloy steels. For this purpose, a dataset of 5,760 images of pipeline surface with and without pitting corrosion was collected. The developed convolutional neural network (CNN) architecture was trained on this dataset. The hyperparameters of this architecture were optimized using Bayesian optimization. The developed and optimized CNN architecture does not have a large number of trainable parameters compared to existing CNN-based architectures. Also, the developed architecture showed significantly higher accuracy of 98.44 %, when classifying images into images without corrosion and with pitting corrosion. The developed CNN outperformed most existing classifiers in its parameters. A pitting corrosion localization system was also developed using the “sliding windows” and “image pyramid” methods, which made it possible to localize areas with identified pitting corrosion on the surface of pipelines made of low carbon and low alloy steels, using the developed CNN without additional labeling of the data set. The proposed deep learning approach will eliminate the need for the operator to visually inspect the pipeline for pitting corrosion, which is costly and time-consuming method.

The research was carried out with financial support of the grant of Russian Scientific Fund No. 22-29-00524,

keywords Energy, industry 4.0, gas pipelines, energy transition, natural resources, non-destructive testing; lifecycle assessment, deep learning, convolutional neural networks, object detection

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