Cover
Vol. 20 No. 1 (2024)

Published: June 30, 2024

Pages: 137-148

Original Article

Internet of Things Based Oil Pipeline Spill Detection System Using Deep Learning and LAB Colour Algorithm

Muhammad H. Obaid 1 Ali H. Hamad 2
1Informatics Institute for Postgraduate Studies, Iraqi Commission for Computers and Informatics, Baghdad,Iraq
2Department of Information and Communication Engineering, University of Baghdad, Baghdad, Iraq
History
  • Received: May 9, 2023
  • Revised: July 21, 2023
  • Accepted: July 22, 2023
  • Available Online: January 15, 2024
DOI

https://doi.org/10.37917/ijeee.20.1.14

Abstract

Given the role that pipelines play in transporting crude oil, which is considered the basis of the global economy and across different environments, hundreds of studies revolve around providing the necessary protection for it. Various technologies have been employed in this pursuit, differing in terms of cost, reliability, and efficiency, among other factors. Computer vision has emerged as a prominent technique in this field, albeit requiring a robust image-processing algorithm for spill detection. This study employs image segmentation techniques to enable the computer to interpret visual information and images effectively. The research focuses on detecting spills in oil pipes caused by leakage, utilizing images captured by a drone equipped with a Raspberry Pi and Pi camera. These images, along with their global positioning system (GPS) location, are transmitted to the base station using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol. At the base station, deep learning techniques, specifically Holistically-Nested Edge Detection (HED) and extreme inception (Xception) networks, are employed for image processing to identify contours. The proposed algorithm can detect multiple contours in the images. To pinpoint a contour with a black color, representative of an oil spill, the CIELAB color space (LAB) algorithm effectively removes shadow effects. If a contour is detected, its area and perimeter are calculated to determine whether it exceeds a certain threshold. The effectiveness of the proposed system was tested on Iraqi oil pipeline systems, demonstrating its capability to detect spills of different sizes.

Keywords

References

  1. S. Adenubi, D. Appah, E. Okafor, and V. Aimikhe, “A review of leak detection systems for natural gas pipelines and facilities,” 2023.
  2. K. G. Aljuaid, M. A. Albuoderman, E. A. AlAhmadi, and J. Iqbal, “Comparative review of pipelines moni- toring and leakage detection techniques,” in 2020 2nd International Conference on Computer and Information Sciences (ICCIS), pp. 1–6, IEEE, 2020.
  3. M. A. Adegboye, W.-K. Fung, and A. Karnik, “Recent advances in pipeline monitoring and oil leakage detec- tion technologies: Principles and approaches,” Sensors, vol. 19, no. 11, p. 2548, 2019.
  4. L. Boaz, S. Kaijage, and R. Sinde, “An overview of pipeline leak detection and location systems,” in Pro- ceedings of the 2nd Pan African International Confer- ence on Science, Computing and Telecommunications (PACT 2014), pp. 133–137, IEEE, 2014.
  5. S. Marathe, “Leveraging drone based imaging technol- ogy for pipeline and rou monitoring survey,” in SPE Asia Pacific Health, Safety, Security, Environment and Social Responsibility Symposium?, p. D021S006R001, SPE, 2019.
  6. J. Allen and B. Walsh, “Enhanced oil spill surveillance, detection and monitoring through the applied technology 147 | Obaid & Hamad of unmanned air systems,” in International oil spill con- ference, vol. 2008, pp. 113–120, American Petroleum Institute, 2008.
  7. T. Bakirman, B. Kulavuz, and B. Bayram, “Use of ar- tificial intelligence toward climate-neutral cultural her- itage,” Photogrammetric Engineering & Remote Sens- ing, vol. 89, no. 3, pp. 163–171, 2023.
  8. M. Fahimipirehgalin, E. Trunzer, M. Odenweller, and B. Vogel-Heuser, “Automatic visual leakage detection and localization from pipelines in chemical process plants using machine vision techniques,” Engineering, vol. 7, no. 6, pp. 758–776, 2021.
  9. O. Ibitoye, O. Shafiq, and A. Matrawy, “A convolutional neural network based solution for pipeline leak detec- tion,” Recruit Researchers, Oct, 2019.
  10. A. Li, D. Ye, E. Lyu, S. Song, M. Q.-H. Meng, and C. W. de Silva, “Rgb-thermal fusion network for leakage detection of crude oil transmission pipes,” in 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 883–888, IEEE, 2019.
  11. Z. Jiao, G. Jia, and Y. Cai, “A new approach to oil spill detection that combines deep learning with unmanned aerial vehicles,” Computers & Industrial Engineering, vol. 135, pp. 1300–1311, 2019.
  12. M. Krestenitis, G. Orfanidis, K. Ioannidis, K. Avgeri- nakis, S. Vrochidis, and I. Kompatsiaris, “Oil spill iden- tification from satellite images using deep neural net- works,” Remote Sensing, vol. 11, no. 15, p. 1762, 2019.
  13. A. Alharam, E. Almansoori, W. Elmadeny, and H. Al- noiami, “Real time ai-based pipeline inspection using drone for oil and gas industries in bahrain,” in 2020 In- ternational Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT), pp. 1–5, IEEE, 2020.
  14. P. Ravishankar, S. Hwang, J. Zhang, I. X. Khalilullah, and B. Eren-Tokgoz, “Darts—drone and artificial intelli- gence reconsolidated technological solution for increas- ing the oil and gas pipeline resilience,” International Journal of Disaster Risk Science, vol. 13, no. 5, pp. 810– 821, 2022.
  15. A. A. Sharafutdinov, F. S. Khafizov, I. F. Khafizov, A. V. Krasnov, A. V. Akhmethafizov, V. I. Zakirova, and A. N. Khafizova, “Development of a method for calculating fire and oil spills parameters,” in AIP Conference Pro- ceedings, vol. 2216, AIP Publishing, 2020.
  16. M. Mahdianpari, B. Salehi, F. Mohammadimanesh, G. Larsen, and D. R. Peddle, “Mapping land-based oil spills using high spatial resolution unmanned aerial vehicle imagery and electromagnetic induction survey data,” Journal of applied remote sensing, vol. 12, no. 3, pp. 036015–036015, 2018.
  17. N. V. S. Korlapati, F. Khan, Q. Noor, S. Mirza, and S. Vaddiraju, “Review and analysis of pipeline leak de- tection methods,” Journal of Pipeline Science and Engi- neering, p. 100074, 2022.
  18. N. V. S. Korlapati, F. Khan, Q. Noor, S. Mirza, and S. Vaddiraju, “Review and analysis of pipeline leak de- tection methods,” Journal of Pipeline Science and Engi- neering, p. 100074, 2022.
  19. Y. Zhou, H. Zhao, and Y. Liu, “An evaluative review of the vtol technologies for unmanned and manned aerial vehicles,” Computer Communications, vol. 149, pp. 356– 369, 2020.
  20. S.-M. Hou, C.-L. Jia, Y.-B. Wanga, and M. Brown, “A review of the edge detection technology,” Sparkling- light Transactions on Artificial Intelligence and Quan- tum Computing (STAIQC), vol. 1, no. 2, pp. 26–37, 2021.
  21. T. Bakirman, B. Kulavuz, and B. Bayram, “Use of ar- tificial intelligence toward climate-neutral cultural her- itage,” Photogrammetric Engineering & Remote Sens- ing, vol. 89, no. 3, pp. 163–171, 2023.
  22. X. Soria, A. Sappa, P. Humanante, and A. Akbarinia, “Dense extreme inception network for edge detection,” Pattern Recognition, vol. 139, p. 109461, 2023.
  23. R. Grompone von Gioi and G. Randall, “A brief analysis of the dense extreme inception network for edge detec- tion,” IPOL. Journal Image Processing On Line, no 12, Oct 2022, pp. 389-403., 2022.
  24. X. Soria, A. Sappa, P. Humanante, and A. Akbarinia, “Dense extreme inception network for edge detection,” Pattern Recognition, vol. 139, p. 109461, 2023.
  25. Z. Tian, D. Chen, S. Liu, and F. Liu, “Dexined-based aluminum alloy grain boundary detection algorithm,” in 2020 Chinese Control And Decision Conference (CCDC), pp. 5647–5652, IEEE, 2020.
  26. Z. Tian, D. Chen, S. Liu, and F. Liu, “Dexined-based aluminum alloy grain boundary detection algorithm,” in 2020 Chinese Control And Decision Conference (CCDC), pp. 5647–5652, IEEE, 2020. 148 | Obaid & Hamad
  27. L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamar´ıa, M. A. Fadhel, M. Al-Amidie, and L. Farhan, “Review of deep learning: Concepts, cnn architectures, challenges, applications, future directions,” Journal of big Data, vol. 8, pp. 1–74, 2021.
  28. R. Grompone von Gioi and G. Randall, “A brief analysis of the holistically-nested edge detector,” IPOL. Journal Image Processing On Line, no 12, Oct 2022, pp. 369- 377, 2022.
  29. N. Yu, Z. Zhang, Q. Xu, E. Firdaous, and J. Lin, “An improved method for cloth pattern cutting based on holistically-nested edge detection,” in 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS), pp. 1246–1251, IEEE, 2021.
  30. I. Misra, M. K. Rohil, S. M. Moorthi, and D. Dhar, “Sprint: Spectra preserving radiance image fusion tech- nique using holistic deep edge spatial attention and min- naert guided bayesian probabilistic model,” Signal Pro- cessing: Image Communication, vol. 113, p. 116920, 2023.
  31. L. Dong, W. Zhang, and W. Xu, “Underwater image enhancement via integrated rgb and lab color models,” Signal Processing: Image Communication, vol. 104, p. 116684, 2022.
  32. N. Abbadi and E. Razaq, “Automatic gray images col- orization based on lab color space,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 18, no. 3, pp. 1501–1509, 2020.
  33. B. Mishra and A. Kertesz, “The use of mqtt in m2m and iot systems: A survey,” IEEE Access, vol. 8, pp. 201071– 201086, 2020.
  34. S. S. Ibraheem, A. H. Hamad, and A. S. A. Jalal, “A secure messaging for internet of things protocol based rsa and dna computing for video surveillance system,” in 2018 Third Scientific Conference of Electrical Engi- neering (SCEE), pp. 280–284, IEEE, 2018.