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PotholeEye+: Deep-Learning Based Pavement Distress Detection System toward Smart Maintenance

Juyoung Park1,*, Jung Hee Lee1, Junseong Bang2,3
1 Korea Expressway Corporation, 77, Hyeoksin8-ro, Gimcheon-si, Korea
2 Electronics and Telecommunications Research Institute (ETRI), 218 Gajeong-ro, Yuseong-gu, Daejun-si, Korea
3 University of Science & Technology (UST), 217 Gajeong-ro, Yuseong-gu, Daejun-si, Korea
* Corresponding Author: Juyoung Park. Email:
(This article belongs to this Special Issue: HPC with Artificial Intelligence based Deep Video Data Analytics: Models, Applications and Approaches)

Computer Modeling in Engineering & Sciences 2021, 127(3), 965-976. https://doi.org/10.32604/cmes.2021.014669

Received 19 October 2020; Accepted 21 January 2021; Issue published 24 May 2021

Abstract

We propose a mobile system, called PotholeEye+, for automatically monitoring the surface of a roadway and detecting the pavement distress in real-time through analysis of a video. PotholeEye+ pre-processes the images, extracts features, and classifies the distress into a variety of types, while the road manager is driving. Every day for a year, we have tested PotholeEye+ on real highway involving real settings, a camera, a mini computer, a GPS receiver, and so on. Consequently, PotholeEye+ detected the pavement distress with accuracy of 92%, precision of 87% and recall 74% averagely during driving at an average speed of 110 km/h on a real highway.


Keywords

Pavement distress; detection; classification; convolutional neural network; deep learning; video analysis

Cite This Article

Park, J., Lee, J. H., Bang, J. (2021). PotholeEye+: Deep-Learning Based Pavement Distress Detection System toward Smart Maintenance. CMES-Computer Modeling in Engineering & Sciences, 127(3), 965–976.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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