Machine Learning-Based Drone Detection and Classification: State-of-the-Art in Research

150

With the increasing number of drones, the danger of their illegal use has become relevant. This has necessitated the creation of automatic drone protection systems. One of the important tasks solved by these systems is the reliable detection of drones near guarded objects. This problem can be solved using various methods. From the point of view of the price–quality ratio, the use of video cameras for a drone detection is of great interest. However, drone detection using visual information is hampered by the large similarity of drones to other objects, such as birds or airplanes. In addition, drones can reach very high speeds, so detection should be done in real time. This paper addresses the problem of real-time drone detection with high accuracy. We divided the drone detection task into two separate tasks: the detection of moving objects and the classification of the detected object into drone, bird, and background. The moving object detection is based on background subtraction, while classification is performed using a convolutional neural network (CNN). The experimental results showed that the proposed approach can achieve an accuracy comparable to existing approaches at high processing speed. We also concluded that the main limitation of our detector is the dependence of its performance on the presence of a moving background.

Section: Introductionmentioning

confidence: 99%

Real-Time and Accurate Drone Detection in a Video with a Static Background

Seidaliyeva

Akhmetov

Ilipbayeva

et al. 2020

150

“…The classification of drones via radar signatures using micro-Doppler information has been investigated using a variety of techniques [ 5 , 6 ]. Some approaches include using features derived from spectrograms [ 7 ], the application of deep learning techniques to range-Doppler maps [ 8 ], and cadence–velocity diagrams [ 9 ], as well as polarimetry [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…Micro-Doppler radar signatures are studied using time-frequency analysis, and most commonly using the Short Time Fourier Transform (STFT) [ 2 , 3 , 5 , 6 , 11 , 12 , 13 , 14 , 15 ]. Depending on the STFT window length, two different types of spectrograms are obtained.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Rotary Drone HERM Line Spectrum under Manoeuvering Conditions

Klaer

Huang

Sévigny

et al. 2020

Detecting and identifying drones is of great interest due to the proliferation of highly manoeuverable drones with on-board sensors of increasing sensing capabilities. In this paper, we investigate the use of radars for tackling this problem. In particular, we focus on the problem of detecting rotary drones and distinguishing between single-propeller and multi-propeller drones using a micro-Doppler analysis. Two different radars were used, an ultra wideband (UWB) continuous wave (CW) C-band radar and an automotive frequency modulated continuous wave (FMCW) W-band radar, to collect micro-Doppler signatures of the drones. By taking a closer look at HElicopter Rotor Modulation (HERM) lines, the spool and chopping lines are identified for the first time in the context of drones to determine the number of propeller blades. Furthermore, a new multi-frequency analysis method using HERM lines is developed, which allows the detection of propeller rotation rates (spool and chopping frequencies) of single and multi-propeller drones. Therefore, the presented method is a promising technique to aid in the classification of drones.

“…The state-of-the-art solutions on drone detection are classified in the function of technologies involved: Visual, acoustic, radio-frequency (RF), and multimodal detection [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

New Approach of UAV Movement Detection and Characterization Using Advanced Signal Processing Methods Based on UWB Sensing

Digulescu

Despina-Stoian

Stanescu

et al. 2020

In the last years, the commercial drone/unmanned aerial vehicles market has grown due to their technological performances (provided by the multiple onboard available sensors), low price, and ease of use. Being very attractive for an increasing number of applications, their presence represents a major issue for public or classified areas with a special status, because of the rising number of incidents. Our paper proposes a new approach for the drone movement detection and characterization based on the ultra-wide band (UWB) sensing system and advanced signal processing methods. This approach characterizes the movement of the drone using classical methods such as correlation, envelope detection, time-scale analysis, but also a new method, the recurrence plot analysis. The obtained results are compared in terms of movement map accuracy and required computation time in order to offer a future starting point for the drone intrusion detection.