Development of a robot vision system for installation progress monitoring

This dissertation researches on the implementation of an industrial solution for construction site progress monitoring by deploying a robot with vision sensors and artificial intelligence techniques. Construction progress monitoring is an important topic for efficient construction project management...

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Main Author: Cheung, Shing Chit
Other Authors: CHEAH Chien Chern
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/141068
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author Cheung, Shing Chit
author2 CHEAH Chien Chern
author_facet CHEAH Chien Chern
Cheung, Shing Chit
author_sort Cheung, Shing Chit
collection NTU
description This dissertation researches on the implementation of an industrial solution for construction site progress monitoring by deploying a robot with vision sensors and artificial intelligence techniques. Construction progress monitoring is an important topic for efficient construction project management with satisfactory quality. This research aims to use a robot for progress monitoring in order to reduce manpower involved and to ensure well-organized supervision on the construction progress. The application scope of this dissertation focuses on the constructions using Prefabricated Prefinished Volumetric Construction (PPVC) method. Thus, this dissertation will research and propose a program including the pre-processing of the images captured in a PPVC construction site, the development of the machine learning and convolutional neural network for object recognition, and the post-processing of the recognition results.
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spelling ntu-10356/1410682023-07-04T16:33:06Z Development of a robot vision system for installation progress monitoring Cheung, Shing Chit CHEAH Chien Chern School of Electrical and Electronic Engineering ECCCheah@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems This dissertation researches on the implementation of an industrial solution for construction site progress monitoring by deploying a robot with vision sensors and artificial intelligence techniques. Construction progress monitoring is an important topic for efficient construction project management with satisfactory quality. This research aims to use a robot for progress monitoring in order to reduce manpower involved and to ensure well-organized supervision on the construction progress. The application scope of this dissertation focuses on the constructions using Prefabricated Prefinished Volumetric Construction (PPVC) method. Thus, this dissertation will research and propose a program including the pre-processing of the images captured in a PPVC construction site, the development of the machine learning and convolutional neural network for object recognition, and the post-processing of the recognition results. Master of Science (Computer Control and Automation) 2020-06-03T12:33:39Z 2020-06-03T12:33:39Z 2020 Thesis-Master by Coursework https://hdl.handle.net/10356/141068 en application/pdf Nanyang Technological University
spellingShingle Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Cheung, Shing Chit
Development of a robot vision system for installation progress monitoring
title Development of a robot vision system for installation progress monitoring
title_full Development of a robot vision system for installation progress monitoring
title_fullStr Development of a robot vision system for installation progress monitoring
title_full_unstemmed Development of a robot vision system for installation progress monitoring
title_short Development of a robot vision system for installation progress monitoring
title_sort development of a robot vision system for installation progress monitoring
topic Engineering::Electrical and electronic engineering::Computer hardware, software and systems
url https://hdl.handle.net/10356/141068
work_keys_str_mv AT cheungshingchit developmentofarobotvisionsystemforinstallationprogressmonitoring