Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach

The objective of this article is to propose and verify a reliable detection mechanism of multi-rotor unmanned aerial vehicles (UAVs). Such a task needs to be solved in many areas such as in the protection of vulnerable buildings or in the protection of privacy. Our system was firstly realized by standard computer vision methods using the Oriented FAST and Rotated BRIEF (ORB) feature detector. Due to the low success rate achieved in real-world conditions, the machine-learning approach was used as an alternative detection mechanism. The “Common Objects in Context dataset” was used as a predefined dataset and it was extended by 1000 samples of UAVs from the SafeShore dataset. The effectiveness and the reliability of our system are proven by four basic experiments—drone in a static image and videos which are displaying a drone in the sky, multiple drones in one image, and a drone with another flying object in the sky. The successful detection rate achieved was 97.3% in optimal conditions.