Road surfaces suffer from sources of deterioration, such as weather conditions, constant
usage, loads, and the age of the infrastructure. These sources of decay generate anomalies …

The paper aims to classify road surface types and conditions by characterizing the temporal
and spectral features of vibration signals gathered from land roads. In the past, road …

Presented herein is a big-data driven methodology for the detection of roadway anomalies,
utilizing smartphone-based data and image signal streams. The methodology uses a …

Roads are a strategic asset of a country and are of great importance for the movement of
passengers and goods. Increasing traffic volume and load, together with the aging of roads …

Recent advances in vehicular technology and sensing devices has led to the proliferation of
different approaches for road surface condition monitoring and road anomaly detection …

Computer vision has evolved in the last decade as a key technology for numerous
applications replacing human supervision. Timely detection of traffic violations and …

This paper addresses road anomaly detection by formulating it as a classification problem
and applying deep learning approaches to solve it. Besides conventional road anomalies …

Road surface hazards affect the driving safety and comfort of road users. Recently,
smartphones and mobile devices equipped with motion sensors such as accelerometers …

Road condition monitoring is essential for improving traffic safety and reducing accidents.
Machine learning methods have recently gained prominence in the practically important task …

Road surface monitoring is a key factor to providing smooth and safe road infrastructure to
road users. The key to road surface condition monitoring is to detect road surface anomalies …