EPS eLibrary: SMALL UAV SYSTEMS CAPABLE OF CARRYING OUT RECONNAISSANCE MISSIONS TO DETECT LEAKS IN OIL AND GAS PIPELINES, AIMING TO LIMIT ACCIDENTAL ENVIRONMENTAL POLLUTION

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Title: SMALL UAV SYSTEMS CAPABLE OF CARRYING OUT RECONNAISSANCE MISSIONS TO DETECT LEAKS IN OIL AND GAS PIPELINES, AIMING TO LIMIT ACCIDENTAL ENVIRONMENTAL POLLUTION

Year

2024

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Peer Reviewed

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Title

SMALL UAV SYSTEMS CAPABLE OF CARRYING OUT RECONNAISSANCE MISSIONS TO DETECT LEAKS IN OIL AND GAS PIPELINES, AIMING TO LIMIT ACCIDENTAL ENVIRONMENTAL POLLUTION

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Authors

Stefan-Mircea Mustata; Cristian Vidan; Ionut-Mihai Nacu

DOI

10.5593/sgem2024v/3.2/s06.49

DOI Issue

10.5593/sgem2024v/3.2

ISSN

1314-2704

ISBN

978-619-7603-76-7

Language

English

Volume

Issue

3.2

Editor

• Prof. DSc. Oleksandr Trofymchuk, UKRAINE • Prof. Dr. hab. oec. Baiba Rivza, LATVIA

Scientific Area

Sustainable Oil, Gas, and Energy Transition Exploration

Abstract/ Foreword

The paper presents an alternative approach for detecting potential oil or gas leaks from transport and distribution pipelines. The detection of possible accidental pollution is carried out using small UAV systems with the help of photogrammetry. Currently, significant material and human resources are allocated to detect such events, and the use of UAVs can solve the problem of monitoring both small areas like oil platforms, gas extraction stations, or oil wells by implementing multirotor-type architectures, as well as long pipeline systems by using fixed-wing systems suitable for endurance flights. An essential aspect of these systems is the versatility shown in use depending on the payload. Thus, the paper considers the use of a wide range of sensors, such as optical sensors in the visible spectrum, gas sensors capable of detecting potential leaks in real time or allowing post-mission analysis of gas concentration levels in different areas of the responsibility zone, as well as LIDAR sensors, which allow mapping of the areas traversed by such transport and distribution systems. Widely studied, the presented technology represents a decisive factor in reducing environmental pollution globally, by limiting accidents involving oil and gas leaks, which are increasingly frequent in the vast existing networks. The paper begins with an overview of the system, continues with methods for mission planning and execution, simulation and actual realization of some scenarios, and concludes with the post-mission analysis of the data provided by the onboard sensors to determine the system's efficiency and the impact of its use, both economically by reducing losses and ecologically.

References (click to open)

[1] Yilmaz, A., Javed, O., & Shah, M. (2006). "Object tracking: A survey." ACM Computing Surveys, 38(4), Article 13.
[2] Smeulders, A. W. M., Chu, D. M., Cucchiara, R., Calderara, S., Dehghan, A., & Shah, M. (2014). "Visual tracking: An experimental survey." IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(7), 1442-1468.
[3] Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016). "Simple online and real-time tracking." In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 3464-3468).
[4] Bar-Shalom, Y., & Li, X. R. (1995). "Multitarget-Multisensor Tracking: Principles and Techniques." YBS Publishing.
[5] Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). "Novel approach to nonlinear/non-Gaussian Bayesian state estimation." IEE Proceedings F (Radar and Signal Processing), 140(2), 107-113.
[6] Danelljan, M., Bhat, G., Khan, F. S., & Felsberg, M. (2017). "Eco: Efficient convolution operators for tracking." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6638-6646).
[7] Bertinetto, L., Valmadre, J., Henriques, J. F., Vedaldi, A., & Torr, P. H. S. (2016). "Fully-convolutional siamese networks for object tracking." In European Conference on Computer Vision (pp. 850-865).
[8] Zhu, Z., Wang, Q., Bo, L., Wu, Y., Yan, J., & Hu, W. (2018). "Distractor-aware Siamese Networks for visual object tracking." In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 101-117).

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Type

conference

Url

https://doi.org/10.5593/sgem2024v/3.2/s06.49

Full manuscript

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Proceedings Тitle

Proceedings of 24th International Multidisciplinary Scientific GeoConference SGEM 2024

Note

24th International Multidisciplinary Scientific GeoConference SGEM 2024, 27 - 30 November, 2024

Type

Proceedings Paper

Publisher

STEF92 Technology

Series

International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM

Organization

SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Russian Acad Arts; Turkish Acad Sci.

Pages

383-390

Date

27 - 30 November, 2024

Online Source Type

website

Digital Source Type

10091

Keywords

oil and gas, environmental protection, UAV, photogrammetry, sensors

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Title: SMALL UAV SYSTEMS CAPABLE OF CARRYING OUT RECONNAISSANCE MISSIONS TO DETECT LEAKS IN OIL AND GAS PIPELINES, AIMING TO LIMIT ACCIDENTAL ENVIRONMENTAL POLLUTION

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Acknowledgements (click to open)

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