Distance insensitive concrete crack detection using convolutional neural network with controlled blurriness

Structural health monitoring system has been implemented to assess structural damage with minimal manpower. Most of research and development interests in structural damage detection have moved towards the use of artificial intelligence to aid in such process. Recent research has highlighted the use...

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Main Author:	Ng, Su Fen
Format:	Thesis
Language:	English
Published:	2019
Subjects:	TA Engineering (General) Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/86088/1/NgSuFenMSKA2019.pdf
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spelling	my-utm-ep.860882020-08-30T08:56:03Z Distance insensitive concrete crack detection using convolutional neural network with controlled blurriness 2019 Ng, Su Fen TA Engineering (General). Civil engineering (General) Structural health monitoring system has been implemented to assess structural damage with minimal manpower. Most of research and development interests in structural damage detection have moved towards the use of artificial intelligence to aid in such process. Recent research has highlighted the use of convolutional neural network (CNN) as one of the powerful tools for accurate and effective image recognition. Nonetheless, the application of CNN on crack damage detection is limited by the inability of the method to detect crack autonomously without a given distance. In view of this, the present study developed a CNN based artificial intelligence for detecting concrete crack autonomously at various distance. The innovation of this study is the use of blurred and sharp images to train CNN. This idea is inspired from the fact that images taken from further distance Eire blurrier. Eight databases with different combination of datasets are then considered and trained on designed CNN. It is found that all networks recorded with at least 95 % accuracy. The robustness and adaptability of the network with the use of sharp images only are tested on twentythree images taken from Universiti Teknologi Malaysia under various conditions. Additionally, these eight networks are evaluated by classifying four different images taken in the distance of 0.5 m, 1.0 m, 1.5 m and 2.0 m, respectively. It is found that the most performing network across various image distances is the network solely made up of image with blurriness level 1. The results show that the presence of blurred images can potentially solve the image distance issue associated with CNN. 2019 Thesis http://eprints.utm.my/id/eprint/86088/ http://eprints.utm.my/id/eprint/86088/1/NgSuFenMSKA2019.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:134342 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Civil Engineering Faculty of Engineering - School of Civil Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) Ng, Su Fen Distance insensitive concrete crack detection using convolutional neural network with controlled blurriness
description	Structural health monitoring system has been implemented to assess structural damage with minimal manpower. Most of research and development interests in structural damage detection have moved towards the use of artificial intelligence to aid in such process. Recent research has highlighted the use of convolutional neural network (CNN) as one of the powerful tools for accurate and effective image recognition. Nonetheless, the application of CNN on crack damage detection is limited by the inability of the method to detect crack autonomously without a given distance. In view of this, the present study developed a CNN based artificial intelligence for detecting concrete crack autonomously at various distance. The innovation of this study is the use of blurred and sharp images to train CNN. This idea is inspired from the fact that images taken from further distance Eire blurrier. Eight databases with different combination of datasets are then considered and trained on designed CNN. It is found that all networks recorded with at least 95 % accuracy. The robustness and adaptability of the network with the use of sharp images only are tested on twentythree images taken from Universiti Teknologi Malaysia under various conditions. Additionally, these eight networks are evaluated by classifying four different images taken in the distance of 0.5 m, 1.0 m, 1.5 m and 2.0 m, respectively. It is found that the most performing network across various image distances is the network solely made up of image with blurriness level 1. The results show that the presence of blurred images can potentially solve the image distance issue associated with CNN.
format	Thesis
qualification_level	Master's degree
author	Ng, Su Fen
author_facet	Ng, Su Fen
author_sort	Ng, Su Fen
title	Distance insensitive concrete crack detection using convolutional neural network with controlled blurriness
title_short	Distance insensitive concrete crack detection using convolutional neural network with controlled blurriness
title_full	Distance insensitive concrete crack detection using convolutional neural network with controlled blurriness
title_fullStr	Distance insensitive concrete crack detection using convolutional neural network with controlled blurriness
title_full_unstemmed	Distance insensitive concrete crack detection using convolutional neural network with controlled blurriness
title_sort	distance insensitive concrete crack detection using convolutional neural network with controlled blurriness
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Civil Engineering
granting_department	Faculty of Engineering - School of Civil Engineering
publishDate	2019
url	http://eprints.utm.my/id/eprint/86088/1/NgSuFenMSKA2019.pdf
_version_	1747818493519069184

Distance insensitive concrete crack detection using convolutional neural network with controlled blurriness

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