Abstract
In recent years, unmanned aerial vehicles have become a popular research platform with many application areas such as military, civil, commercial and recreational areas, thanks to their high maneuverability, vertical take-off / landing, and outdoor and indoor use. Today, small, light, and very high powerful embedding systems have been developed. Therefore, many real-time computer vision applications can be run on unmanned aerial vehicle platforms by integrating such embedding systems onto these vehicles. In this work, the problem of car detection (localization) in images taken from unmanned aerial vehicles has been studied. To this end, we collected a new aerial image dataset by using quadcopters and different type of cameras. To solve the car detection problem, the results were compared by using both the Polyhedral Conic Classifier and the You Only Look Once (YOLO) algorithm which is considered one of the fastest deep neural network methods in the literature.
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