Autoencoder-based key-frame identification for water pipeline CCTV inspection
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Jiao, Yutong
issued: 2021-02-24T20:44:14Z
issued: 2021
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Closed-circuit television (CCTV) is being widely adopted in water pipeline inspection. The inspector needs to spend a long time to watch the recorded video during the office-based survey and can get fatigue easily. An automated process can release the inspector's workload and ensure the consistent quality of the survey. However, a fully automated survey of varied structural discontinuities still remains as a challenge. This study aims to first identify the key frames of the CCTV video, which contain the major anomalies captured from the internal surface of the pipe. Thus, the inspector can focus more on these key frames. First, the key-frame identification task is performed by using an unsupervised learning-based framework with state-of-the-art autoencoders. Three popular autoencoders were implemented in this framework, and results were collected accordingly. The experimental results illustrate that this framework achieves up to 0.940 accuracy with a relatively lower precision metric of 0.901. Secondly, a key-frame identification framework based on the steerable pyramid autoencoder (SPAE) is proposed in order to improve the accuracy of this task. Both the parameter optimization and comparative studies for the proposed SPAE were carried out in this research. Explicitly, the SPAE develops good capability of representation learning and extraction. The experimental results demonstrate that the SPAE-based approach can achieve 0.984 accuracy, which outperforms the other experimental methods selected for comparison. Thirdly, a log-Gabor autoencoder (LGAE) based framework is proposed to further enhance the performance on key-frame identification in terms of representation learning. It can be regarded as an improved version of the SPAE-based approach. Comparative studies were conducted, and the results show that this novel LGAE takes advantage of the feature detection over the other methods with the accuracy of 0.988 and the recall metric of 0.996. Additionally, the LGAE has…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/77378
note: AUTOENCODER-BASEDKEY-FRAME IDENTIFICATION FORWATER PIPELINE CCTVINSPECTIONbyYutong JiaoB.Eng., Beijing University of Chemical Technology, 2018A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Electrical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)February 2021c© Yutong Jiao, 2021The following individuals certify that they have read, and recommend to the College ofGraduate Studies for acceptance, a thesis entitled:AUTOENCODER-BASED KEY-FRAME IDENTIFICATION FOR WATER PIPELINE CCTVINSPECTIONsubmitted by Yutong Jiao in partial fulfilment of the requirements of the degree of Master ofApplied ScienceDr. Zheng Liu, School of EngineeringSupervisorDr. Kenneth Chau, School of EngineeringSupervisory Committee MemberDr. Yang Cao, School of EngineeringSupervisory Committee MemberDr. Joshua Brinkerhoff, School of EngineeringUniversity ExamineriiAbstractClosed-circuit television (CCTV) is being widely adopted in water pipeline inspection.The inspector needs to spend a long time to watch the recorded video during the office-basedsurvey and can get fatigue easily. An automated process can release the inspector’s workloadand ensure the consistent quality of the survey. However, a fully automated survey of variedstructural discontinuities still remains as a challenge. This study aims to first identify the keyframes of the CCTV video, which contain the major anomalies captured from the internalsurface of the pipe. Thus, the inspector can focus more on these key frames.First, the key-frame identification task is performed by using an unsupervised learning-based framework with state-of-the-art autoencoders. Three popular autoencoders were imple-mented in this framework, and results were collected accordingly. The experimental resultsillustrate that this framework achieves up to 0.940 accuracy with a relatively lower precisionmetric of 0.901.Secondly, a key-frame identification framework bas…
has type: Thesis/Dissertation
date issued: 2021-05
is shown at: 10.14288/1.0395959
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
type: Text
identifier URI: http://hdl.handle.net/2429/77378