Imperial College London
Alternate

Dr Simon Hu

Faculty of Engineering, Department of Civil and Environmental Engineering

Honorary Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 6024j.s.hu05

 
 
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Location

 

422Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Wu:2020:10.3390/s20195564,
author = {Wu, C and Wang, Z and Hu, S and Lepine, J and Na, X and Ainalis, D and Stettler, M},
doi = {10.3390/s20195564},
journal = {Sensors},
pages = {1--23},
title = {An automated machine-learning approach for road pothole detection using smartphone sensor data},
url = {http://dx.doi.org/10.3390/s20195564},
volume = {20},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness.
AU  - Wu,C
AU  - Wang,Z
AU  - Hu,S
AU  - Lepine,J
AU  - Na,X
AU  - Ainalis,D
AU  - Stettler,M
DO  - 10.3390/s20195564
EP  - 23
PY  - 2020///
SN  - 1424-8220
SP  - 1
TI  - An automated machine-learning approach for road pothole detection using smartphone sensor data
T2  - Sensors
UR  - http://dx.doi.org/10.3390/s20195564
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000586399000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.mdpi.com/1424-8220/20/19/5564
UR  - http://hdl.handle.net/10044/1/87902
VL  - 20
ER  -
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