Assessment of the Condition of Pipelines Using Convolutional Neural Networks
Pipelines are structural elements of many systems. For example, they are used in water supply and heat supply systems, in chemical production facilities, aircraft manufacturing, and in the oil and gas industry. Accidents in piping systems result in significant economic damage. An important factor fo...
Main Authors: | Yuri Vankov, Aleksey Rumyantsev, Shamil Ziganshin, Tatyana Politova, Rinat Minyazev, Ayrat Zagretdinov |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-02-01
|
Series: | Energies |
Subjects: | |
Online Access: | https://www.mdpi.com/1996-1073/13/3/618 |
Similar Items
-
Convolutional neural networks and local binary patterns for hyperspectral image classification
by: Xiangpo Wei, et al.
Published: (2019-01-01) -
Learning Traffic as Images for Incident Detection Using Convolutional Neural Networks
by: Xiaozhou Liu, et al.
Published: (2020-01-01) -
Parallel accelerator design for convolutional neural networks based on FPGA
by: Wang Ting, et al.
Published: (2021-02-01) -
Assessment of Crack in Corrosion Defects in Natural Gas Transmission Pipelines
by: Hosseini, Seyed Aliakbar
Published: (2010) -
Assessment of Crack in Corrosion Defects in Natural Gas Transmission Pipelines
by: Hosseini, Seyed Aliakbar
Published: (2010)