A Deep Learning-Based Approach for Pipeline Cracks Monitoring

Wael A. Altabey; Sallam A. Kouritem; Mohammed I. Abouheaf; Nabil Nahas

doi:10.1109/ICECCME55909.2022.9987998

A Deep Learning-Based Approach for Pipeline Cracks Monitoring

Wael A. Altabey
, Sallam A. Kouritem
, Mohammed I. Abouheaf
, Nabil Nahas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Scopus citations

Abstract

In order to improve the efficiency and accuracy of pipeline surface cracks monitoring based on image processing, the Convolutional Neural Network (CNN) algorithm in target detection is introduced to quickly identify the type, location, and area for the extracted cracks area with borders, the CNN based on crack contour network (CCN) method used to locate and extract the crack shape. CCN algorithm introduces the accuracy rate (P%), recall rate (R%), and F-score (F%) index to evaluate the algorithm in the problem during cracks monitoring, and determines the corresponding contour area of the crack frame according to the maximum F-score. A pipeline image was carried out by using an inspection drone with high definition camera. The results show the recognition efficiency and accuracy of the proposed method. After the optimal value of the degree threshold, the accuracy rate, recall rate, and F-score are recorded 91. 8%, 86. 1%, and 84.6%, respectively.

Original language	English
Title of host publication	International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665470957
DOIs	https://doi.org/10.1109/ICECCME55909.2022.9987998
State	Published - 2022
Externally published	Yes
Event	2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022 - Male, Maldives Duration: 16 Nov 2022 → 18 Nov 2022

Publication series

Name	International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

Conference

Conference	2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022
Country/Territory	Maldives
City	Male
Period	16/11/22 → 18/11/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Convolutional Neural Network (CNN)
Crack contour Network (CCN) method
Image processing
Pipeline crack detection

ASJC Scopus subject areas

Automotive Engineering
Electrical and Electronic Engineering
Mechanical Engineering
Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Renewable Energy, Sustainability and the Environment

Access to Document

10.1109/ICECCME55909.2022.9987998

Cite this

Altabey, W. A., Kouritem, S. A., Abouheaf, M. I., & Nahas, N. (2022). A Deep Learning-Based Approach for Pipeline Cracks Monitoring. In International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022 (International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECCME55909.2022.9987998

Altabey, Wael A. ; Kouritem, Sallam A. ; Abouheaf, Mohammed I. et al. / A Deep Learning-Based Approach for Pipeline Cracks Monitoring. International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022).

@inproceedings{3326c9aeb37148bb8a842ee5631918ac,

title = "A Deep Learning-Based Approach for Pipeline Cracks Monitoring",

abstract = "In order to improve the efficiency and accuracy of pipeline surface cracks monitoring based on image processing, the Convolutional Neural Network (CNN) algorithm in target detection is introduced to quickly identify the type, location, and area for the extracted cracks area with borders, the CNN based on crack contour network (CCN) method used to locate and extract the crack shape. CCN algorithm introduces the accuracy rate (P\%), recall rate (R\%), and F-score (F\%) index to evaluate the algorithm in the problem during cracks monitoring, and determines the corresponding contour area of the crack frame according to the maximum F-score. A pipeline image was carried out by using an inspection drone with high definition camera. The results show the recognition efficiency and accuracy of the proposed method. After the optimal value of the degree threshold, the accuracy rate, recall rate, and F-score are recorded 91. 8\%, 86. 1\%, and 84.6\%, respectively.",

keywords = "Convolutional Neural Network (CNN), Crack contour Network (CCN) method, Image processing, Pipeline crack detection",

author = "Altabey, \{Wael A.\} and Kouritem, \{Sallam A.\} and Abouheaf, \{Mohammed I.\} and Nabil Nahas",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022 ; Conference date: 16-11-2022 Through 18-11-2022",

year = "2022",

doi = "10.1109/ICECCME55909.2022.9987998",

language = "English",

series = "International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022",

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}

Altabey, WA, Kouritem, SA, Abouheaf, MI & Nahas, N 2022, A Deep Learning-Based Approach for Pipeline Cracks Monitoring. in International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022. International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022, Institute of Electrical and Electronics Engineers Inc., 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022, Male, Maldives, 16/11/22. https://doi.org/10.1109/ICECCME55909.2022.9987998

A Deep Learning-Based Approach for Pipeline Cracks Monitoring. / Altabey, Wael A.; Kouritem, Sallam A.; Abouheaf, Mohammed I. et al.
International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Deep Learning-Based Approach for Pipeline Cracks Monitoring

AU - Altabey, Wael A.

AU - Kouritem, Sallam A.

AU - Abouheaf, Mohammed I.

AU - Nahas, Nabil

PY - 2022

Y1 - 2022

N2 - In order to improve the efficiency and accuracy of pipeline surface cracks monitoring based on image processing, the Convolutional Neural Network (CNN) algorithm in target detection is introduced to quickly identify the type, location, and area for the extracted cracks area with borders, the CNN based on crack contour network (CCN) method used to locate and extract the crack shape. CCN algorithm introduces the accuracy rate (P%), recall rate (R%), and F-score (F%) index to evaluate the algorithm in the problem during cracks monitoring, and determines the corresponding contour area of the crack frame according to the maximum F-score. A pipeline image was carried out by using an inspection drone with high definition camera. The results show the recognition efficiency and accuracy of the proposed method. After the optimal value of the degree threshold, the accuracy rate, recall rate, and F-score are recorded 91. 8%, 86. 1%, and 84.6%, respectively.

AB - In order to improve the efficiency and accuracy of pipeline surface cracks monitoring based on image processing, the Convolutional Neural Network (CNN) algorithm in target detection is introduced to quickly identify the type, location, and area for the extracted cracks area with borders, the CNN based on crack contour network (CCN) method used to locate and extract the crack shape. CCN algorithm introduces the accuracy rate (P%), recall rate (R%), and F-score (F%) index to evaluate the algorithm in the problem during cracks monitoring, and determines the corresponding contour area of the crack frame according to the maximum F-score. A pipeline image was carried out by using an inspection drone with high definition camera. The results show the recognition efficiency and accuracy of the proposed method. After the optimal value of the degree threshold, the accuracy rate, recall rate, and F-score are recorded 91. 8%, 86. 1%, and 84.6%, respectively.

KW - Convolutional Neural Network (CNN)

KW - Crack contour Network (CCN) method

KW - Image processing

KW - Pipeline crack detection

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U2 - 10.1109/ICECCME55909.2022.9987998

DO - 10.1109/ICECCME55909.2022.9987998

M3 - Conference contribution

AN - SCOPUS:85146440321

T3 - International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

BT - International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

Y2 - 16 November 2022 through 18 November 2022

ER -

Altabey WA, Kouritem SA, Abouheaf MI, Nahas N. A Deep Learning-Based Approach for Pipeline Cracks Monitoring. In International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022). doi: 10.1109/ICECCME55909.2022.9987998

A Deep Learning-Based Approach for Pipeline Cracks Monitoring

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this