Deep learning for pipeline damage detection: an overview of the concepts and a survey of the state-of-the-art

Raheleh Jafari, Sina Razvarz, Alexander Gegov, Boriana Vatchova

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Pipelines have been extensively implemented to transfer oil as well as gas products at wide distances as they are safe, and suitable. However, numerous sorts of damages may happen to the pipeline, for instance erosion, cracks, and dent. Hence, if these faults are not properly refit will result in the pipeline demolitions having leak or segregation which leads to tremendously environment risks. Deep learning methods aid operators to recognize the earliest phases of threats to the pipeline, supplying them time and information in order to handle the problem efficiently. This paper illustrates fundamental implications of deep learning comprising convolutional neural networks. Furthermore the usages of deep learning approaches for hampering pipeline detriment through the earliest diagnosis of threats are introduced.

Original languageEnglish
Title of host publication2020 IEEE 10th International Conference on Intelligent Systems, IS 2020 - Proceedings
EditorsVassil Sgurev, Vladimir Jotsov, Rudolf Kruse, Mincho Hadjiski
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781728154565
ISBN (Print)9781728154572
Publication statusPublished - 18 Sept 2020
Event10th IEEE International Conference on Intelligent Systems, IS 2020 - Sofia, Bulgaria
Duration: 28 Aug 202030 Aug 2020

Publication series

NameIEEE International Conference on Intelligent Systems
ISSN (Print)1541-1672


Conference10th IEEE International Conference on Intelligent Systems, IS 2020


  • convolutional neural network
  • damage detection
  • deep learning

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