Multivariate Analysis of Concrete Image Using Thermography and Edge Detection

With the growing demand for structural health monitoring system applications, data imaging is an ideal method for performing regular routine maintenance inspections. Image analysis can provide invaluable information about the health conditions of a structure’s existing infrastructure by recording an...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Bubryur Kim, Se-Woon Choi, Gang Hu, Dong-Eun Lee, Ronnie O. Serfa Juan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	crack analysis concrete cumulative distribution function edge detection Sobel edge detection Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/4f0a49b421dc4debabdd40092487bb5c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:4f0a49b421dc4debabdd40092487bb5c
record_format	dspace
spelling	oai:doaj.org-article:4f0a49b421dc4debabdd40092487bb5c2021-11-11T19:19:19ZMultivariate Analysis of Concrete Image Using Thermography and Edge Detection10.3390/s212173961424-8220https://doaj.org/article/4f0a49b421dc4debabdd40092487bb5c2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7396https://doaj.org/toc/1424-8220With the growing demand for structural health monitoring system applications, data imaging is an ideal method for performing regular routine maintenance inspections. Image analysis can provide invaluable information about the health conditions of a structure’s existing infrastructure by recording and analyzing exterior damages. Therefore, it is desirable to have an automated approach that reports defects on images reliably and robustly. This paper presents a multivariate analysis approach for images, specifically for assessing substantial damage (such as cracks). The image analysis provides graph representations that are related to the image, such as the histogram. In addition, image-processing techniques such as grayscale are also implemented, which enhance the object’s information present in the image. In addition, this study uses image segmentation and a neural network, for transforming an image to analyze it more easily and as a classifier, respectively. Initially, each concrete structure image is preprocessed to highlight the crack. A neural network is used to calculate and categorize the visual characteristics of each region, and it shows an accuracy for classification of 98%. Experimental results show that thermal image extraction yields better histogram and cumulative distribution function features. The system can promote the development of various thermal image applications, such as nonphysical visual recognition and fault detection analysis.Bubryur KimSe-Woon ChoiGang HuDong-Eun LeeRonnie O. Serfa JuanMDPI AGarticlecrack analysisconcretecumulative distribution functionedge detectionSobel edge detectionChemical technologyTP1-1185ENSensors, Vol 21, Iss 7396, p 7396 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	crack analysis concrete cumulative distribution function edge detection Sobel edge detection Chemical technology TP1-1185
spellingShingle	crack analysis concrete cumulative distribution function edge detection Sobel edge detection Chemical technology TP1-1185 Bubryur Kim Se-Woon Choi Gang Hu Dong-Eun Lee Ronnie O. Serfa Juan Multivariate Analysis of Concrete Image Using Thermography and Edge Detection
description	With the growing demand for structural health monitoring system applications, data imaging is an ideal method for performing regular routine maintenance inspections. Image analysis can provide invaluable information about the health conditions of a structure’s existing infrastructure by recording and analyzing exterior damages. Therefore, it is desirable to have an automated approach that reports defects on images reliably and robustly. This paper presents a multivariate analysis approach for images, specifically for assessing substantial damage (such as cracks). The image analysis provides graph representations that are related to the image, such as the histogram. In addition, image-processing techniques such as grayscale are also implemented, which enhance the object’s information present in the image. In addition, this study uses image segmentation and a neural network, for transforming an image to analyze it more easily and as a classifier, respectively. Initially, each concrete structure image is preprocessed to highlight the crack. A neural network is used to calculate and categorize the visual characteristics of each region, and it shows an accuracy for classification of 98%. Experimental results show that thermal image extraction yields better histogram and cumulative distribution function features. The system can promote the development of various thermal image applications, such as nonphysical visual recognition and fault detection analysis.
format	article
author	Bubryur Kim Se-Woon Choi Gang Hu Dong-Eun Lee Ronnie O. Serfa Juan
author_facet	Bubryur Kim Se-Woon Choi Gang Hu Dong-Eun Lee Ronnie O. Serfa Juan
author_sort	Bubryur Kim
title	Multivariate Analysis of Concrete Image Using Thermography and Edge Detection
title_short	Multivariate Analysis of Concrete Image Using Thermography and Edge Detection
title_full	Multivariate Analysis of Concrete Image Using Thermography and Edge Detection
title_fullStr	Multivariate Analysis of Concrete Image Using Thermography and Edge Detection
title_full_unstemmed	Multivariate Analysis of Concrete Image Using Thermography and Edge Detection
title_sort	multivariate analysis of concrete image using thermography and edge detection
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4f0a49b421dc4debabdd40092487bb5c
work_keys_str_mv	AT bubryurkim multivariateanalysisofconcreteimageusingthermographyandedgedetection AT sewoonchoi multivariateanalysisofconcreteimageusingthermographyandedgedetection AT ganghu multivariateanalysisofconcreteimageusingthermographyandedgedetection AT dongeunlee multivariateanalysisofconcreteimageusingthermographyandedgedetection AT ronnieoserfajuan multivariateanalysisofconcreteimageusingthermographyandedgedetection
_version_	1718431527482687488

Multivariate Analysis of Concrete Image Using Thermography and Edge Detection

Ejemplares similares