A three-tier road condition classification system using a spiking neural network model

Road surface anomaly detection and classification based on crowd-sourced smart phone sensor data has become an important area of research over the last decade due to its potential benefits to road maintenance. Previous studies focused on paved roads in which anomaly classification were modelled as single-staged events mostly using machine learning and threshold-based methods. Little or no attention has been paid to road type classification and anomaly detection and classification on unpaved roads, which constitute a larger percentage of roads in the developing world. In this paper, road condition classification is approached as a multi-tier activity, comprising of models for road type classification, anomaly classification models for paved roads as well as unpaved roads using a novel Spiking Neural Network (SNN) learning model. To demonstrate the viability of the proposed system, road condition data for the various tasks were collected via an Android Application developed by the authors from which statistical features were extracted and used to train and evaluate the models. Experimental results showed that the proposed SNN model yielded significantly higher classification performance when compared to a Support Vector Machine (SVM) and Multilayer Perceptron (MLP) trained and tested using the collected datasets and classification models reported in existing studies.