Jafari, R orcid.org/0000-0001-7298-2363, Razvarz, S, Gegov, A et al. (1 more author) (2020) Deep Learning for Pipeline Damage Detection: an Overview of the Concepts and a Survey of the State-of-the-Art. In: 2020 IEEE 10th International Conference on Intelligent Systems (IS). 10th IEEE International Conference on Intelligent Systems IS’20, 28-30 Aug 2020, Varna, Bulgaria. Institute of Electrical and Electronics Engineers ISBN 978-1-7281-5457-2
Abstract
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.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | ©2020 IEEE. This is an author produced version of a paper accepted for publication in Proceedings of the 10th IEEE International Conference on Intelligent Systems. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | deep learning; convolutional neural network; damage detection |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Arts, Humanities and Cultures (Leeds) > School of Design (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Jan 2020 10:26 |
Last Modified: | 08 Dec 2020 12:31 |
Published Version: | https://ieeexplore.ieee.org/document/9200137 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/IS48319.2020.9200137 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:156089 |