RoadEye: Road Condition Monitoring using Computer Vision and Deep Learning Techniques

Christos Theoharatos


The RoadEye project proposes the development and demonstration of an integrated application (or system) for real-time road condition monitoring, using a camera and an embedded system, which can be integrated in complete ADAS systems that provide a full range of functions. This application will be able to track and detect the condition of the road surface in real-time, within a distance of 5-to-25 meters from the vehicle, based on computer vision and ma-chine/deep learning techniques. The techniques that are being developed within the project will be able to classify the state of the road into some preselected categories such as normal road and slippery road (e.g. wet, snow etc.), and even detect surface anomalies within the road such as potholes and speed bumps / humps.


Road condition monitoring system; Computer vision; Deep learning; Embedded systems; Heterogeneous computing

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