Video data analytics for the automation of water pipeline inspection
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Rayhana, Rakiba
issued: 2023-01-05T23:48:15Z
issued: 2023
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Water transmission networks are a community's most expensive and critical urban assets. Management of these pipelines is becoming one of the significant concerns as some of the water pipelines are at the end of their service lifespan. Nowadays, robotic platforms equipped with cameras and a wheeled system are used to videotape the internal state of the water pipelines. The acquired videos then require certified human personnel to assess the conditions of water pipelines manually. However, manual assessment is often error-prone, challenging, and expensive. Additionally, while inspecting the pipeline, the robotic platform experiences difficulties to pass through the valves installed inside the pipeline. This disrupts the inspection process and adds labor to a human operator. Hence, this research aims to mitigate the aforementioned issues of the inspection process by developing automated video data analytical frameworks to enhance inspection operations. At first, an extensive literature review was conducted, showing that automated frameworks display better performance when developed using deep learning-based techniques. Therefore, a deep learning-based defect characterization framework and a user-friendly defect characterization interface are developed to simultaneously detect, classify and segment the defects of water pipelines. The framework is developed by employing three residual networks (ResNet), a channel-spatial attention mechanism (CSA), Mask Canny-Regional Convolution Neural Network (MaskC-RCNN), and an ensemble mechanism. The results from this experiment depicted that the proposed model improved the defect detection rate and saved time for human operators. Then, a deep learning-based valve detection framework is developed to detect the valves by combining MobileNet-160 and Feature Pyramid Network (FPN). The experimental results and analysis shows that the developed algorithm detected the valves efficiently and accurately. Lastly, a real-time and super-lightwe…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/83594
note: VIDEO DATA ANALYTICS FOR THEAUTOMATION OFWATER PIPELINEINSPECTIONbyRakiba RayhanaB.Sc., BRAC University, 2015M.Sc., The University of Strathclyde, 2017A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinTHE COLLEGE OF GRADUATE STUDIES(Electrical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)December 2022© Rakiba Rayhana, 2022The following individuals certify that they have read, and recommend to the Col-lege of Graduate Studies for acceptance, the dissertation entitled:VIDEO DATA ANALYTICS FOR THE AUTOMATION OF WATER PIPELINEINSPECTIONsubmitted by Rakiba Rayhana in partial fulfillment of the requirements of the de-gree of Doctor of Philosophy.Dr. Zheng Liu, School of EngineeringSupervisorDr. Rehan Sadiq, School of EngineeringSupervisory Committee MemberDr. Jahangir Hossain, School of EngineeringSupervisory Committee MemberDr. Mohamed Shehata, Department of Computer Science, Physics, Mathematics,and StatisticsUniversity ExaminerDr. Lucio Soibelman, Sonny Astani Department of Civil and EnvironmentalEngineering, University of Southern CaliforniaExternal ExamineriiAbstractWater transmission networks are a community’s most expensive and critical urbanassets. Management of these pipelines is becoming one of the significant concernsas some of the water pipelines are at the end of their service lifespan. Nowadays,robotic platforms equipped with cameras and a wheeled system are used to video-tape the internal state of the water pipelines. The acquired videos then requirecertified human personnel to assess the conditions of water pipelines manually.However, manual assessment is often error-prone, challenging, and expensive.Additionally, while inspecting the pipeline, the robotic platform experiences diffi-culties to pass through the valves installed inside the pipeline. This disrupts the in-spection process and adds labor to a human operator. Hence, this research aims tomitigate the aforementioned issues of the inspect…
has type: Thesis/Dissertation
date issued: 2023-02
is shown at: 10.14288/1.0422986
language: eng
degree discipline: Electrical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Liu, Zheng (Professor of engineering)
type: Text
identifier URI: http://hdl.handle.net/2429/83594