Identification of Road Pavement Types Using Bayesian Analysis and Neural Networks

A new method to classify and identify different types of road pavements by analysing the near field sound profile and texture using statistical learning methods is proposed. A set of characteristics were extracted from the noise profile and from the road surface texture. Sound measurements were carried out following the close-proximity method with the texture descriptors being provided by a high speed profilometer system. As a first approach, it is assumed that the features extracted from the noise and texture characteristics follow normal distributions. However, this assumption is not completely verified for all types of road surfaces. The method presented herein exploits the use of Bayesian analysis complemented by a neural network in order to improve the classification results.