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Paper lc3-2017-109:
Detecting Pavement Patches Utilizing Smartphones Technology and Vehicles

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Charalambos Kyriakou and Symeon E. Christodoulou

Detecting Pavement Patches Utilizing Smartphones Technology and Vehicles

Abstract: Presented herein is a study on the utilization of low-cost technology for detection of roadway pavement anomalies (patches and potholes), by use of sensors on smartphones and of automobiles’ on-board diagnostic (OBD-II) devices for the collection and analysis of vibration-related data while vehicles are in movement. The mobile data collection kit consists of a triaxial accelerometer, a gyroscope and a global positioning sensor. The smartphone-based data collection is complimented with robust regression analysis and a bagged-trees classification model for the classification of pavement anomalies. The proposed system is readily available, low-cost and adequately accurate, and can be utilized in crowd-sourced applications for pavement monitoring. Further, the proposed methodology has been field-tested, exhibiting detection accuracy levels higher than 90% for pavement patches, and it is currently expanded to include larger datasets and a bigger number of pavement defect types.

Keywords: Pavement Anomalies, Detection and Classification, Smartphones Technology, Robust Regression, Bagged Trees

DOI: https://doi.org/10.24928/JC3-2017/0109

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Series: jc3:2017 (browse)
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