Detection of loaded and unloaded UAV using deep neural network

Ulzhalgas Seidaliyeva; Manal Alduraibi; Lyazzat Ilipbayeva; Akhan Almagambetov

doi:10.1109/IRC.2020.00093

Detection of loaded and unloaded UAV using deep neural network

Ulzhalgas Seidaliyeva, Manal Alduraibi, Lyazzat Ilipbayeva, Akhan Almagambetov

Department of Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

29 Citations (Scopus)

Abstract

Unmanned aerial vehicles or drones quickly became cheaper, becoming more advanced and affordable to the general public. And the ease of control made them popular among people, who want to deliver various suspicious loads. UAV detection can be performed by different existing techniques, such as radar, radio frequency, acoustic and optical sensing techniques. Because of low-cost and low-power technology computer vision is considered as an effective method for detecting Unmanned aerial vehicles. Previous studies of UAV detection mostly have dealt with detection of UAV existence. The primary aim of this paper is to review recent research into the UAV detection and perform single stage loaded and unloaded UAV detection based on YOLOv2.

Original language	English
Title of host publication	2020 Fourth IEEE International Conference on Robotic Computing (IRC)
Pages	490-494
Number of pages	5
DOIs	https://doi.org/10.1109/IRC.2020.00093
Publication status	Published - Nov 1 2020

Keywords

Visualization
Radar detection
Phantoms
Detectors
Unmanned aerial vehicles
Optical sensors
Payloads
UAV detection
computer vision
YOLOv2
deep learning

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10.1109/IRC.2020.00093

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@inproceedings{11654ed93fa54544b1e07b126aebf679,

title = "Detection of loaded and unloaded UAV using deep neural network",

abstract = "Unmanned aerial vehicles or drones quickly became cheaper, becoming more advanced and affordable to the general public. And the ease of control made them popular among people, who want to deliver various suspicious loads. UAV detection can be performed by different existing techniques, such as radar, radio frequency, acoustic and optical sensing techniques. Because of low-cost and low-power technology computer vision is considered as an effective method for detecting Unmanned aerial vehicles. Previous studies of UAV detection mostly have dealt with detection of UAV existence. The primary aim of this paper is to review recent research into the UAV detection and perform single stage loaded and unloaded UAV detection based on YOLOv2.",

keywords = "Visualization, Radar detection, Phantoms, Detectors, Unmanned aerial vehicles, Optical sensors, Payloads, UAV detection, computer vision, YOLOv2, deep learning",

author = "Ulzhalgas Seidaliyeva and Manal Alduraibi and Lyazzat Ilipbayeva and Akhan Almagambetov",

year = "2020",

month = nov,

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doi = "10.1109/IRC.2020.00093",

language = "English",

pages = "490--494",

booktitle = "2020 Fourth IEEE International Conference on Robotic Computing (IRC)",

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AU - Seidaliyeva, Ulzhalgas

AU - Alduraibi, Manal

AU - Ilipbayeva, Lyazzat

AU - Almagambetov, Akhan

PY - 2020/11/1

Y1 - 2020/11/1

N2 - Unmanned aerial vehicles or drones quickly became cheaper, becoming more advanced and affordable to the general public. And the ease of control made them popular among people, who want to deliver various suspicious loads. UAV detection can be performed by different existing techniques, such as radar, radio frequency, acoustic and optical sensing techniques. Because of low-cost and low-power technology computer vision is considered as an effective method for detecting Unmanned aerial vehicles. Previous studies of UAV detection mostly have dealt with detection of UAV existence. The primary aim of this paper is to review recent research into the UAV detection and perform single stage loaded and unloaded UAV detection based on YOLOv2.

AB - Unmanned aerial vehicles or drones quickly became cheaper, becoming more advanced and affordable to the general public. And the ease of control made them popular among people, who want to deliver various suspicious loads. UAV detection can be performed by different existing techniques, such as radar, radio frequency, acoustic and optical sensing techniques. Because of low-cost and low-power technology computer vision is considered as an effective method for detecting Unmanned aerial vehicles. Previous studies of UAV detection mostly have dealt with detection of UAV existence. The primary aim of this paper is to review recent research into the UAV detection and perform single stage loaded and unloaded UAV detection based on YOLOv2.

KW - Visualization

KW - Radar detection

KW - Phantoms

KW - Detectors

KW - Unmanned aerial vehicles

KW - Optical sensors

KW - Payloads

KW - UAV detection

KW - computer vision

KW - YOLOv2

KW - deep learning

U2 - 10.1109/IRC.2020.00093

DO - 10.1109/IRC.2020.00093

M3 - Conference contribution

SP - 490

EP - 494

BT - 2020 Fourth IEEE International Conference on Robotic Computing (IRC)

ER -

Detection of loaded and unloaded UAV using deep neural network

Abstract

Keywords

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