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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Bibliographic Details
Published inIEEE journal of selected topics in applied earth observations and remote sensing Vol. 11; no. 10; pp. 3701 - 3712
Main Authors Pan, Yifan, Zhang, Xianfeng, Cervone, Guido, Yang, Liping
Format Journal Article
LanguageEnglish
Published IEEE 01.10.2018
Subjects
Artificial neural network (ANN)
Asphalt
asphalt roads
Feature extraction
Image segmentation
multispectral imagery
pavement distress
random forest (RF)
Remote sensing
Roads
support vector machine (SVM)
Surface cracks
unmanned aerial vehicle (UAV)
Unmanned aerial vehicles
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Summary: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.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2018.2865528