Deep Learning Approach for Pitting Corrosion Detection in Gas Pipelines

The paper introduces a computer vision methodology for detecting pitting corrosion in gas pipelines. To achieve this, a dataset comprising 576,000 images of pipelines with and without pitting corrosion was curated. A custom-designed and optimized convolutional neural network (CNN) was employed for b...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-05, Vol.24 (11), p.3563
Main Authors: Malashin, Ivan, Tynchenko, Vadim, Nelyub, Vladimir, Borodulin, Aleksei, Gantimurov, Andrei, Krysko, Nikolay V, Shchipakov, Nikita A, Kozlov, Denis M, Kusyy, Andrey G, Martysyuk, Dmitry, Galinovsky, Andrey
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Automation
Classification
Computer vision
Corrosion
Corrosion and anti-corrosives
Datasets
Deep learning
deep neural network
Defects
Gas industry
Gas pipelines
Gas transmission industry
Machine learning
Machine vision
Methods
Neural networks
Pipelines
pitting corrosion
Repair & maintenance
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Summary:The paper introduces a computer vision methodology for detecting pitting corrosion in gas pipelines. To achieve this, a dataset comprising 576,000 images of pipelines with and without pitting corrosion was curated. A custom-designed and optimized convolutional neural network (CNN) was employed for binary classification, distinguishing between corroded and non-corroded images. This CNN architecture, despite having relatively few parameters compared to existing CNN classifiers, achieved a notably high classification accuracy of 98.44%. The proposed CNN outperformed many contemporary classifiers in its efficacy. By leveraging deep learning, this approach effectively eliminates the need for manual inspection of pipelines for pitting corrosion, thus streamlining what was previously a time-consuming and cost-ineffective process.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24113563