# Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach
## 1 Introduction  
Object recognition algorithms and their classification are based on the fact that the considered objects have common characteristics. This means that features are not defined only by their appearance but also by the behavior or the way of movement. The issue of unmanned aerial vehicle (UAV) recognition in the sky is focused on objects that could occur in the sensing area as well as on the UAVs themselves. One of the basic visual properties of a UAV is its shape. Each type of UAV (from the tricopter to the octocopter) looks very similar in its category. A tricopter has the shape of an equilateral triangle, a quadcopter has the shape of a square, etc. Moreover, each UAV is composed of rigid construction that has its own visual characteristics. This construction usually includes storage space for the control board in the middle and from three to eight arms depending on the number of propellers. Each arm is finished by the motor on which the propeller is suspended. This is the basic UAV appearance pattern which can be used for almost every single UAV. An exception may be the UAVs from the nano and mini categories, where the propeller and the motors can be mounted directly on the center panel of the referenced UAV. One of the main advantages of these machines is their extended battery life which makes this drone type able to operate for a longer time in the air [1] and also fly higher [2]. Some of the fixed-winged drones as well as the multi-rotor models can take off from the ground, but most of them can take off and stay in the air only during movement. There is also another type of drone that differs from the multi-rotor models in size, weight, and the maximum achievable speed. These drones are called fixed-winged drones [3]. Due to their properties, they are often used in applications such as environment and area mapping (with the possibility offurther processing and generation of three-dimensional data [4]), in meteorology [2] or for inspection of quality (an interesting example is the inspection of electrical wiring described in [5]). According to [1], fixed-winged UAVs are suitable for the mentioned applications mainly because of their purchase price, maintenance, and operational time. According to [2], another significant advantage is their operation range and their flight safety. Using multi-rotor models, according to [1], there is a better possibility of specific environmental mapping and the main advantages include better use in civilian applications and simple maneuvering. As stated in [6], the growing trend of using these machines will require a certain amount of research and regulation in the future, to ensure their safe usage without restricting airspace, traffic flows, and their efficient management. UAVs are also characterized by the presence (or absence) of several individual components. Some UAVs, for example, are covered by a ring located around the entire periphery of the machine. On the other hand, other UAV types have some parts completely uncovered. Some types of UAV also use an additional covering ring, which may or may not be located in the vicinity of the machine propellers. Most UAVs are equipped with a camera. The camera can be located within the body of the UAV, where it becomes visually less noticeable. However, on UAVs with better-quality cameras, the camera is located on the bottom of the UAV, where it forms a large visually noticeable part. Several types of UAV are characterized also by additional devices. For example, in large-scale UAVs, there are often accessories such as robotic arms or other devices. These aspects, i.e., large additional devices, are not considered in the proposed detection and identification procedure. The main purpose of this work is to design and implement an automated detection approach whose properties and reliability can be compared with the common human observer staring at the sky. The proposed approach will be able to recognize multi-rotor drones based on their appearance.