# Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach
## 1 Introduction
We focus our attention on these drone models mainly because of their versatility of use in common civil applications. Considering the detection of the UAV itself, the fact that some UAVs have a light device on the lower part of the body can make the detection more effective especially during unfavorable illumination conditions. Another feature that characterizes a UAV is the way of movement. The purpose of the movement is to move the UAV from the stabilized position from the start point to the endpoint at which the machine is to be stabilized again. The UAV movement itself is characterized by its monotony. Contrary to the movement of a bird sliding or waving its wings, the UAV does not perform any such movements. The basic analysis of a UAV's movement was performed in our research, and from the results it is clear that these machines are moving according to certain patterns. The UAV movement is mostly linear but it can suddenly change direction, speed, or height. The UAV can also stabilize its position and float in place without moving. These movements are considerably different from those of birds. There have been several reports of UAV abuse for privacy or terrorist attacks. Some cases are also known where a UAV has been used for bringing contraband materials into prisons. These, as well as many other potential misuses of these machines, create the need to identify and track UAVs in an area where such attacks may occur. Due to the features and versatility of the use of these machines, there are many possibilities for their misuse. These machines can fly very low to the ground, but also very high. They can disguise themselves behind various objects and with the help of additional devices, they can fly in different lighting conditions and overcome various obstacles. However, we assume that these machines would have a problem operating during adverse conditions such as high wind, rain, snow, smog, or hail. We do not deny the possibility of using machines that are adapted to such conditions, but in our work, we assume a significantly reduced ability to maneuver or use additional equipment. Because the UAVs can move quickly and undetected in many areas they can create significant risks. The UAV thus becomes an ideal machine for disrupting safety, endangering life, causing damage, or making an unauthorized entry to private land or tracking people. For these reasons, different regulations arise. In the Slovak Republic Decision no. 2/2019 from 14 November 2019 refers to the precise division of UAVs into groups to which specific regulations apply. It also refers to the precisely defined conditions under which unmanned aircraft can be used in the airspace of the country. In general, we can divide the basic approaches of UAV detection into visual and acoustic. Visual detection is realized by camera systems capable of recording a two-dimensional image (cameras operating in the dark can be used as well). These approaches could be based on the appearance or movement of the object, or their combination. Acoustic methods are using microphones [7]. An interesting advantage of acoustic methods is the possibility of detection even when the drone is not in sight [8]. These two approaches can be also combined to create a hybrid detection system. An example is an anti-drone system described in [9], which uses a combination of audio, video, radio-frequency sensors, and a radio-frequency jamming unit. The article is structured as follows: The second section introduces the existing methods for object recognition in an image. The basic analysis of several methods is described. The third section describes the proposed drone detection system that uses standard computer vision methods. The fourth section describes another detection mechanism that uses TensorFlow. The fifth section introduces several experiments with the evaluation of the success rate. The sixth section concludes the paper and proposes future work.