||Detecting Pavement Patches Utilizing Smartphones Technology and Vehicles
||Charalambos Kyriakou and Symeon E. Christodoulou
||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 automobilesÕ 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.
|Year of publication:
||Pavement Anomalies, Detection and Classification, Smartphones Technology, Robust Regression, Bagged Trees
Charalambos Kyriakou and Symeon E. Christodoulou (2017).
Detecting Pavement Patches Utilizing Smartphones Technology and Vehicles. Lean and Computing in Construction Congress (LC3): Volume I Ð Proceedings of the Joint Conference on Computing in Construction (JC3), July 4-7, 2017, Heraklion, Greece, pp. 859-866,