ArticleSensors (Basel, Switzerland)2024
Deep Learning Approach for Pitting Corrosion Detection in Gas Pipelines.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Ethical and Legal Concerns of Deepfake Technology in Biomedical Imaging: A Comprehensive Survey.Annals of biomedical engineering · 2026Review
- Explainable Deep Learning Framework for Binary Corrosion Image Classification Using Grad-CAM.Sensors (Basel, Switzerland) · 2025Article
- An Improved HRNetV2-Based Semantic Segmentation Algorithm for Pipe Corrosion Detection in Smart City Drainage Networks.Journal of imaging · 2025Article
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Authors and funding
11 authors.
Funding
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Abstract
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.
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Registered trials
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