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Open AccessArticle

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

1
Department of Electrical Engineering, Telecommunications and Space Technologies, Satbayev University, Almaty 050000, Kazakhstan
2
Department of Radio Engineering, Electronics and Telecommunications, International IT university, Almaty 050000, Kazakhstan
3
Department of Computer and Information Technology, Purdue University, West Lafayette, IN 47907-2021, USA
*
Author to whom correspondence should be addressed.
Current address: 401 North Grant Street, KNOY 255, West Lafayette, IN 47907-2021, USA
These authors contributed equally to this work.
Sensors 2020, 20(14), 3856; https://doi.org/10.3390/s20143856
Received: 6 June 2020 / Revised: 5 July 2020 / Accepted: 7 July 2020 / Published: 10 July 2020
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. View Full-Text
Keywords: unmanned aerial vehicles; object detection; deep learning; computer vision; image processing; drone detection; UAV detection; visual detection unmanned aerial vehicles; object detection; deep learning; computer vision; image processing; drone detection; UAV detection; visual detection
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MDPI and ACS Style

Seidaliyeva, U.; Akhmetov, D.; Ilipbayeva, L.; Matson, E.T. Real-Time and Accurate Drone Detection in a Video with a Static Background. Sensors 2020, 20, 3856.

AMA Style

Seidaliyeva U, Akhmetov D, Ilipbayeva L, Matson ET. Real-Time and Accurate Drone Detection in a Video with a Static Background. Sensors. 2020; 20(14):3856.

Chicago/Turabian Style

Seidaliyeva, Ulzhalgas; Akhmetov, Daryn; Ilipbayeva, Lyazzat; Matson, Eric T. 2020. "Real-Time and Accurate Drone Detection in a Video with a Static Background" Sensors 20, no. 14: 3856.

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