Detection of Asphalt Pavement Potholes and Cracks Based on the Unmanned Aerial Vehicle Multispectral Imagery
Asphalt roads are the basic component of a land transportation system, and the quality of asphalt roads will decrease during the use stage because of the aging and deterioration of the road surface. In the end, some road pavement distresses may appear on the road surface, such as the most common pot...
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doaj-797dd33bcc4c4d80bf32c1619d876d382021-06-02T23:06:53ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352018-01-0111103701371210.1109/JSTARS.2018.28655288454262Detection of Asphalt Pavement Potholes and Cracks Based on the Unmanned Aerial Vehicle Multispectral ImageryYifan Pan0https://orcid.org/0000-0002-7324-4036Xianfeng Zhang1https://orcid.org/0000-0002-2475-4558Guido Cervone2Liping Yang3https://orcid.org/0000-0002-9240-5501Institute of Remote Sensing and Geographic Information System, Peking University, Beijing, ChinaInstitute of Remote Sensing and Geographic Information System, Peking University, Beijing, ChinaGeoInformatics and Earth Observation Laboratory, Pennsylvania State University, University Park, PA, USAGeoInformatics and Earth Observation Laboratory, Pennsylvania State University, University Park, PA, USAAsphalt roads are the basic component of a land transportation system, and the quality of asphalt roads will decrease during the use stage because of the aging and deterioration of the road surface. In the end, some road pavement distresses may appear on the road surface, such as the most common potholes and cracks. In order to improve the efficiency of pavement inspection, currently some new forms of remote sensing data without destructive effect on the pavement are widely used to detect the pavement distresses, such as digital images, light detection and ranging, and radar. Multispectral imagery presenting spatial and spectral features of objects has been widely used in remote sensing application. In our study, the multispectral pavement images acquired by unmanned aerial vehicle (UAV) were used to distinguish between the normal pavement and pavement damages (e.g., cracks and potholes) using machine learning algorithms, such as support vector machine, artificial neural network, and random forest. Comparison of the performance between different data types and models was conducted and is discussed in this study, and indicates that a UAV remote sensing system offers a new tool for monitoring asphalt road pavement condition, which can be used as decision support for road maintenance practice.https://ieeexplore.ieee.org/document/8454262/Artificial neural network (ANN)asphalt roadsmultispectral imagerypavement distressrandom forest (RF)support vector machine (SVM) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yifan Pan Xianfeng Zhang Guido Cervone Liping Yang |
spellingShingle |
Yifan Pan Xianfeng Zhang Guido Cervone Liping Yang Detection of Asphalt Pavement Potholes and Cracks Based on the Unmanned Aerial Vehicle Multispectral Imagery IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Artificial neural network (ANN) asphalt roads multispectral imagery pavement distress random forest (RF) support vector machine (SVM) |
author_facet |
Yifan Pan Xianfeng Zhang Guido Cervone Liping Yang |
author_sort |
Yifan Pan |
title |
Detection of Asphalt Pavement Potholes and Cracks Based on the Unmanned Aerial Vehicle Multispectral Imagery |
title_short |
Detection of Asphalt Pavement Potholes and Cracks Based on the Unmanned Aerial Vehicle Multispectral Imagery |
title_full |
Detection of Asphalt Pavement Potholes and Cracks Based on the Unmanned Aerial Vehicle Multispectral Imagery |
title_fullStr |
Detection of Asphalt Pavement Potholes and Cracks Based on the Unmanned Aerial Vehicle Multispectral Imagery |
title_full_unstemmed |
Detection of Asphalt Pavement Potholes and Cracks Based on the Unmanned Aerial Vehicle Multispectral Imagery |
title_sort |
detection of asphalt pavement potholes and cracks based on the unmanned aerial vehicle multispectral imagery |
publisher |
IEEE |
series |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
issn |
2151-1535 |
publishDate |
2018-01-01 |
description |
Asphalt roads are the basic component of a land transportation system, and the quality of asphalt roads will decrease during the use stage because of the aging and deterioration of the road surface. In the end, some road pavement distresses may appear on the road surface, such as the most common potholes and cracks. In order to improve the efficiency of pavement inspection, currently some new forms of remote sensing data without destructive effect on the pavement are widely used to detect the pavement distresses, such as digital images, light detection and ranging, and radar. Multispectral imagery presenting spatial and spectral features of objects has been widely used in remote sensing application. In our study, the multispectral pavement images acquired by unmanned aerial vehicle (UAV) were used to distinguish between the normal pavement and pavement damages (e.g., cracks and potholes) using machine learning algorithms, such as support vector machine, artificial neural network, and random forest. Comparison of the performance between different data types and models was conducted and is discussed in this study, and indicates that a UAV remote sensing system offers a new tool for monitoring asphalt road pavement condition, which can be used as decision support for road maintenance practice. |
topic |
Artificial neural network (ANN) asphalt roads multispectral imagery pavement distress random forest (RF) support vector machine (SVM) |
url |
https://ieeexplore.ieee.org/document/8454262/ |
work_keys_str_mv |
AT yifanpan detectionofasphaltpavementpotholesandcracksbasedontheunmannedaerialvehiclemultispectralimagery AT xianfengzhang detectionofasphaltpavementpotholesandcracksbasedontheunmannedaerialvehiclemultispectralimagery AT guidocervone detectionofasphaltpavementpotholesandcracksbasedontheunmannedaerialvehiclemultispectralimagery AT lipingyang detectionofasphaltpavementpotholesandcracksbasedontheunmannedaerialvehiclemultispectralimagery |
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1721400215537188864 |