Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/218220
DC FieldValue
dc.titleIMPROVING DEEP LEARNING-BASED FACADE VISUAL INSPECTION: A DATA QUALITY PERSPECTIVE
dc.contributor.authorGUO JINGJING
dc.date.accessioned2022-03-31T18:00:49Z
dc.date.available2022-03-31T18:00:49Z
dc.date.issued2021-12-10
dc.identifier.citationGUO JINGJING (2021-12-10). IMPROVING DEEP LEARNING-BASED FACADE VISUAL INSPECTION: A DATA QUALITY PERSPECTIVE. ScholarBank@NUS Repository.
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/218220
dc.description.abstractThe main objective of this thesis is to improve the performance of deep learning-based façade visual inspection. In this thesis, the performance is considered from the aspects of reliability and efficiency. To achieve this objective, this thesis designs a research methodology based on the theory of total data quality management. The methodology includes four phases: definition, assessment, analysis, and improvement. The research framework is unfolded through three procedures: data selection, data annotation, and model training. For each procedure, criteria are designed to assess the data quality, and solutions are developed enabling the target stage to focus on “better” data. The experiment results demonstrate that the proposed solutions improved the accuracy and stability of the façade defects detection. Besides, the detection results obtained by the proposed solutions provide more effective outcomes for condition evaluation. Meanwhile, the time and cost are saved in general perspective because no extra labor works are expended.
dc.language.isoen
dc.subjectFacade visual inspection, Deep learning, Data quality problem, Condition evaluation, Total data quality management
dc.typeThesis
dc.contributor.departmentTHE BUILT ENVIRONMENT
dc.contributor.supervisorWang Qian
dc.description.degreePh.D
dc.description.degreeconferredDOCTOR OF PHILOSOPHY (CDE-BE)
dc.identifier.orcid0000-0003-2047-6703
Appears in Collections:Ph.D Theses (Open)

Show simple item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
PhD dissertation_Guo Jingjing(A0192117J).pdf6.8 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

46
checked on Feb 2, 2023

Download(s)

14
checked on Feb 2, 2023

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.