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There is a rapid growth of applications of Unmanned Aerial Vehicles (UAVs) intraffic management, such as traffic surveillance, monitoring, and incidentdetection. However, the existing literature lacks solutions to real-timeincident detection while addressing privacy issues in practice. This studyexplored real-time vehicle detection algorithms on both visual and infraredcameras and conducted experiments comparing their performance. Red Green Blue(RGB) videos and thermal images were collected from a UAS platform alonghighways in the Tampa, Florida, area. Experiments were designed to quantify theperformance of a real-time background subtraction-based method in vehicledetection from a stationary camera on hovering UAVs under free-flow conditions.Several parameters were set in the experiments based on the geometry of thedrone and sensor relative to the roadway. The results show that a backgroundsubtraction-based method can achieve good detection performance on RGB images(F1 scores around 0.9 for most cases), and a more varied performance is seen onthermal images with different azimuth angles. The results of these experimentswill help inform the development of protocols, standards, and guidance for theuse of drones to detect highway congestion and provide input for thedevelopment of incident detection algorithms.

Comparison of Object Detection Algorithms Using Video and Thermal Images  Collected from a UAS Platform: An Application of Drones in Traffic Management