Condition Assessment of Unpaved Roads Using Low-Cost Computer Vision–Based Solutions
Publication: Journal of Transportation Engineering, Part B: Pavements
Volume 149, Issue 1
Abstract
Unpaved roads are an important part of the road transportation system of many countries and they contribute to the accessibility of remote communities and businesses. Despite the importance of unpaved road networks on social and economic development of remote regions, research on semiautomated and automated assessment of these roads is limited. This paper proposes low-cost computer vision–based solutions for assessment of unpaved roads using two approaches: unmanned aerial vehicle (UAV) and participatory-based imaging methods. Both methods use deep neural network to process captured images and locate major road distresses, including potholes, rutting, and corrugations. In addition, a method is proposed to estimate the size of detected potholes in the UAV-captured video frames. Each of the proposed methods was evaluated using a set of experiments, which demonstrated promising performance in assessment of these infrastructure assets that are vital for reliable access of rural and remote communities.
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Data Availability Statement
Some or all test and training data, models, or codes that support the findings of this research study are available from the corresponding author upon reasonable request, specifically digital images, videos, and MATLAB codes.
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Published In
Journal of Transportation Engineering, Part B: Pavements
Volume 149 • Issue 1 • March 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jun 27, 2021
Accepted: Sep 16, 2022
Published online: Nov 14, 2022
Published in print: Mar 1, 2023
Discussion open until: Apr 14, 2023
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Cited by
- Pranav R. T. Peddinti, Harish Puppala, Byungmin Kim, Pavement Monitoring Using Unmanned Aerial Vehicles: An Overview, Journal of Transportation Engineering, Part B: Pavements, 10.1061/JPEODX.PVENG-1291, 149, 3, (2023).