Underwater Pipeline Leakage Detection Using Vision Based Techniques: Semi-AUV (SAUV) Approach

This thesis intends to convert a Remote Operated Vehicle (ROV) to a Semi-Autonomous Underwater Vehicle (SAUV) using a vision-based control system. The SAUV was used for automatic underwater gas pipeline tracking and leakage detection. the leakages in the pipeline using Computer Vision. The SAUV was designed to operate both manually and automatically in underwater conditions. The proposed SAUV has 6 thrusters to achieve 4 degrees of freedom controlled by the controller unit and powered by LiPo battery packs. Our underwater vehicle is equipped with sensors providing continuous feedback signals to automatically control the vehicle to track predefined trajectories. The SAUV can be self-stabilized as the center of gravity and center of buoyancy of the vehicle is positioned in such a way in the predefined plan. The SAUV captures images to perform line tracking along with the pipeline and gas bubble images during its mission. The multi-core umbilical cable is used here for the video signal, the feedback signal, and battery charging lines. This will be used only for development and test purposes and will be removed during autonomous missions. For performing all operations, various control schemes such as computer vision algorithm for object detection using python programming, OpenCV, Hough Transform Theory, etc. are applied. The proposed SAUV is expected to pave the way for the development of advanced underwater oil and gas pipeline industrial applications by ocean scientists.

Name thesis:degree_name

M.A.Sc.

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Windsor

Year dc:date.issued

2021

Author dc:creator

• Thobhani, anisha Alkarim

Advisors dc:contributor.advisor

• S. Alirezaee
• M. Saif

Contributors dc:contributor

• scholarship@uwindsor.ca

Licence

• http://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language.iso

en_CA

Handle dc:identifier.uri

https://hdl.handle.net/20.500.14776/9431

OAI identifier oai:identifier

oai:uwindsor.scholaris.ca:20.500.14776/9431