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Unmanned Aerial Vehicles are increasingly being used in surveillance andtraffic monitoring thanks to their high mobility and ability to cover areas atdifferent altitudes and locations. One of the major challenges is to use aerialimages to accurately detect cars and count them in real-time for trafficmonitoring purposes. Several deep learning techniques were recently proposedbased on convolution neural network (CNN) for real-time classification andrecognition in computer vision. However, their performance depends on thescenarios where they are used. In this paper, we investigate the performance oftwo state-of-the-art CNN algorithms, namely Faster R-CNN and YOLOv3, in thecontext of car detection from aerial images. We trained and tested these twomodels on a large car dataset taken from UAVs. We demonstrated in this paperthat YOLOv3 outperforms Faster R-CNN in sensitivity and processing time,although they are comparable in the precision metric.

Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3