1. Главная
  2. Ресурсы
  3. Библиотечный поиск
Deep LearningApproach for PittingCorrosion Detectionin Gas Pipelines Malashin IvanTynchenko VadimNelyub Vladimir2024 год

Deep Learning Approach for Pitting Corrosion Detection in Gas Pipelines
статья из журнала

Дополнительные ссылки: eLibrary
База данных: Каталог библиотеки СФУ
Библиографическое описание: Deep Learning Approach for Pitting Corrosion Detection in Gas Pipelines : научное издание / I. I. Malashin [et al.]. - Текст : непосредственный // Sensors. - 2024. - Т. 24, № 11. - P. 3563. - ISSN 1424-8220. - ISSN 1424-3210, DOI 10.3390/s24113563.
Аннотация: <jats:p>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.</jats:p>
Год издания: 2024
Авторы: 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
Издательство: Molecular Diversity Preservation International
Источник: Sensors
Выпуск: Т. 24, № 11
Номера страниц: 3563
Держатель оригинала документа: Artificial Intelligence Technology Scientific and Education Center, Department of Welding, Diagnostics and Special Robotics, Bauman Moscow State Technical University, 105005 Moscow, Russia, Scientific Department, Far Eastern Federal University, 690922 Vladivostok, Russia
Количество экземпляров:
Твёрдая копия издания отсутствует в фонде библиотеки

Похожие публикации

Assessment ofAnisotropic AcousticProperties inAdditivelyManufacturedMaterials:Experimental,Computational, and… Malashin IvanTynchenko VadimMartysyuk Dmitry2024 год

Assessment of Anisotropic Acoustic Properties in Additively Manufactured Materials: Experimental,… : статья из журнала

  • Malashin Ivan и др.
  • 2024 год
Controlled Porosityof Selective LaserMelting-ProducedThermal Pipes:Experimental Analysisand Machine Learning… Malashin IvanMartysyuk DmitryTynchenko Vadim2024 год

Controlled Porosity of Selective Laser Melting-Produced Thermal Pipes: Experimental Analysis and… : статья из журнала

  • Malashin Ivan и др.
  • 2024 год
Deep learning forproperty predictionof natural fiberpolymer composites Malashin Ivan P.Martysyuk DmitryNelyub Vladimir2025 год

Deep learning for property prediction of natural fiber polymer composites : научное издание

  • Malashin Ivan P. и др.
  • 2025 год

Electrochemical Aptasensor for Detection of Lung Cancer Tumor Markers in Blood Plasma : статья из журнала

  • Glazyrin Yuri и др.
  • 2024 год
Cross-domain deeptransfer learning forbranching structuresegmentation Shariaty F.Pavlov V. A.Medvedeva E. A.2026 год

Cross-domain deep transfer learning for branching structure segmentation : статья из журнала

  • Shariaty F. и др.
  • 2026 год
Flood SusceptibilityAssessment in UrbanAreas via Deep NeuralNetwork Approach Panfilova TatyanaKukartsev VladislavTynchenko Vadim2024 год

Flood Susceptibility Assessment in Urban Areas via Deep Neural Network Approach : научное издание

  • Panfilova Tatyana и др.
  • 2024 год
Evaluating thegeneralizationability of deeplearning models forsound sourcelocalization Shahoud Ghiath M.Agafonov Evgeny D.2025 год

Evaluating the generalization ability of deep learning models for sound source localization : научное издание

  • Shahoud Ghiath M. и др.
  • 2025 год
Machine learningmodel for the BIMclassification in IFCformat Petrochenko M. V.Nedviga P. N.Kukina A. A.2024 год

Machine learning model for the BIM classification in IFC format : статья из журнала

  • Petrochenko M. V. и др.
  • 2024 год

Показать ещё

Convolutional neuralnetwork and deepmachine learning forECG analysis Nepomnyashchiy O. V.Mohammed Muanis I. AI-SagheerKhaidukova V. N.2024 год

Convolutional neural network and deep machine learning for ECG analysis : статья из журнала

  • Nepomnyashchiy O. V. и др.
  • 2024 год
Convolutional neuralnetwork and deepmachine learning forECG analysis Nepomnyashchiy O.V.Mohammed Muanis I. Al-SagheerKhaidukova V.N.2024 год

Convolutional neural network and deep machine learning for ECG analysis : научное издание

  • Nepomnyashchiy O.V. и др.
  • 2024 год
Application ofmachine learningalgorithms and neuralnetworks foranalyzing theinfluence of datatype in hate… Mbele Ossiyi L. P.Drobintsev P. D.Ustinov S. M.2025 год

Application of machine learning algorithms and neural networks for analyzing the influence of… : статья из журнала

  • Mbele Ossiyi L. P. и др.
  • 2025 год
Extending deeplearning approachesfor forestdisturbancesegmentation on veryhigh-resolutionsatellite images Kislov D.E.Korznikov K.A.Vozmishcheva A.S.2021 год

Extending deep learning approaches for forest disturbance segmentation on very high-resolution… : научное издание

  • Kislov D.E. и др.
  • 2021 год
Predicting DiffusionCoefficients inNafion Membranesduring the SoakingProcess Using aMachine LearningApproach Malashin IvanDaibagya DaniilTynchenko Vadim2024 год

Predicting Diffusion Coefficients in Nafion Membranes during the Soaking Process Using a Machine… : статья из журнала

  • Malashin Ivan и др.
  • 2024 год
Transprise: A novelmachine learningapproach foreukaryotic promoterprediction Pachganov StepanMurta KhalimatZarubin Aleksei2019 год

Transprise: A novel machine learning approach for eukaryotic promoter prediction : статья из журнала

  • Pachganov Stepan и др.
  • 2019 год
Aptamer-basedapproaches forsensitive detectionand epitope mappingof SARS-CoV-2 spikeprotein Poolsup SuttineeYaghoobi ElnazRadchanka Aliaksandra2026 год

Aptamer-based approaches for sensitive detection and epitope mapping of SARS-CoV-2 spike protein : научное издание

  • Poolsup Suttinee и др.
  • 2026 год

Device and Method of Sample Preparation for Gas Chromatographic determination of Volatile Organic… : статья из журнала

  • Оберенко А. В. и др.
  • 2021 год