According to the recent world bank report, around 80% of a life-cycle cost of a road is devoted to maintenance which includes monitoring and repair processes. To more effectively keep road serviceability, knowing the current status of road health is crucial. Typically, the monitoring processes are handled by human-intensive procedures. So, automating this task could lead to saving time and cost and also, improve efficiency. Although, this task has been optimized by Ground Vehicles further. Yet it lacks the disadvantages of human-intensive procedure. As they are still semi-manual, creates traffic issues, and not being eco-friendly or cost efficient. In recent years, UAVs have been successfully utilized to handle a wide range of labor-based tasks including road assessments. This paper presents a drone-based solution to automate road monitoring and segmentation as well as addressing the practical challenges of using drones for this purpose. To do so, a platform is developed that controls a drone through a road monitoring flight using computer vision-based techniques. The platform, rather than sending maneuvering commands to the drone during a flight starting from takeoff to landing, firstly could detect road boundaries by finding vanishing points, and secondly, could identify the dash lines and the center of the road. Finally, the captured road is segmented and labeled with the temporal and geographical information supplied by the Inertial Measurement Unit (IMU) of the drone. It has been tried to optimize the platform in order to handle all the processes in real-time while the UAV is following the road during a flight. To evaluate the proposed idea, the developed platform is tested in urban areas. The achieved results demonstrate how effectively could detect and segment a road in different environments using an off-the-shelf UAV. This platform could improve the automation of the data gathering process required in road maintenance.

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