An Automatic Image Recognition System for Winter Road Condition Monitoring

Abstract

Municipalities and contractors in Canada and other parts of the world rely on road surface condition information during and after a snow storm to optimize maintenance operations and planning. With an ever increasing demand for safer and more sustainable road network there is an ever increasing demand for more reliable, accurate and up-to-date road surface condition information while working with the limited available resources. Such high dependence on road condition information is driving more and more attention towards analyzing the reliability of current technology as well as developing new and more innovative methods for monitoring road surface condition. This research provides an overview of the various road condition monitoring technologies in use today. A new machine vision based mobile road surface condition monitoring system is proposed which has the potential to produce high spatial and temporal coverage. The proposed approach uses multiple models calibrated according to local pavement color and environmental conditions potentially providing better accuracy compared to a single model for all conditions. Once fully developed, this system could potentially provide intermediate data between the more reliable xed monitoring stations, enabling the authorities with a wider coverage without a heavy extra cost. The up to date information could be used to better plan maintenance strategies and thus minimizing salt use and maintenance costs.