Improving Pavement Anomaly Detection Using Backward Feature Elimination

  • Jun-Lin LinEmail author
  • Zhi-Qiang Peng
  • Robert K. Lai
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 288)


Early approaches for pavement anomaly detection used simple heuristic on a small number of features and their associated thresholds. Such methods may not be suitable for data collected from heterogeneous sources (e.g., different vehicles, pavements, inertial sensors, etc.). Instead of manually selecting a set of features and their thresholds, we propose using backward feature elimination on a large set of features such that the optimal set of features can be determined. Our experimental results show that the features selected by backward feature elimination yield the best performance, compared to using all features from the sampled data of the accelerometer and gyrometer.


Accelerometer Gyrometer Pothole detection Backward feature elimination 



This research is supported by the Ministry of Science and Technology, Taiwan, under Grant 105-2632-H-155-022.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Information ManagementYuan Ze UniversityTaoyuanTaiwan
  2. 2.Innovation Center for Big Data and Digital ConvergenceYuan Ze UniversityTaoyuanTaiwan
  3. 3.Department of Computer Science and EngineeringYuan Ze UniversityTaoyuanTaiwan

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