Towards Automatic Evaluation of Asphalt Irregularity Using Smartphone’s Sensors

Abstract

The quality of the pavement of roads and streets has significant influence in the final price of goods and services, in the safety of pedestrians and also in the driver’s comfort. Thus, the development of tools for continuous monitoring of the pavement, intending to obtain a more precise and adequate maintenance plan is essential. In order to reduce the manual effort of inspections made by experts, the use of high-cost equipment as laser profilometer and allowing evaluations in real-time, the use of motion sensor of smartphones to monitor the asphalt irregularity is proposed. In this paper, the present problem is modeled as a classification task that can be performed by supervised learning algorithms and aided by signal processing techniques for features extraction from the acceleration data. The proposed approach shows promising accuracies for the identification of asphalt irregularity (around 99%) and for identification of obstacles as speed bumps, raised crosswalk, pavement markers, and asphalt patches (around 87%).