@inproceedings{171174bd70294a9aaf328b719133620c,
title = "Deep learning for pipeline damage detection: an overview of the concepts and a survey of the state-of-the-art",
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.",
keywords = "convolutional neural network, damage detection, deep learning",
author = "Raheleh Jafari and Sina Razvarz and Alexander Gegov and Boriana Vatchova",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 10th IEEE International Conference on Intelligent Systems, IS 2020 ; Conference date: 28-08-2020 Through 30-08-2020",
year = "2020",
month = sep,
day = "18",
doi = "10.1109/IS48319.2020.9200137",
language = "English",
isbn = "9781728154572",
series = "IEEE International Conference on Intelligent Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "178--182",
editor = "Vassil Sgurev and Vladimir Jotsov and Rudolf Kruse and Mincho Hadjiski",
booktitle = "2020 IEEE 10th International Conference on Intelligent Systems, IS 2020 - Proceedings",
address = "United States",
}