Deep Learning Based Malicious Drone Detection Using Acoustic and Image Data

被引：7

作者：

Kim, Juann ^{[1
]}

Lee, Dongwhan ^{[2
]}

Kim, Youngseo ^{[3
]}

Shin, Heeyeon ^{[4
]}

Heo, Yeeun ^{[5
]}

Wang, Yaqin ^{[6
]}

Matson, Eric T. ^{[6
]}

机构：

[1] Sangmyung Univ, Dept Software, Cheonan, South Korea

[2] Kyung Hee Univ, Software Convergence, Yongin, South Korea

[3] Sangmyung Univ, Dept Human Ctr AI, Seoul, South Korea

[4] Kyung Hee Univ, Comp Engn, Yongin, South Korea

[5] Soongsil Univ, Software Engn, Seoul, South Korea

[6] Purdue Univ, Comp & Informat Technol, W Lafayette, IN 47907 USA

来源：

2022 SIXTH IEEE INTERNATIONAL CONFERENCE ON ROBOTIC COMPUTING, IRC | 2022年

关键词：

drone detection; audio classification; computer vision; counter unmanned aerial systems; deep learning;

D O I：

10.1109/IRC55401.2022.00024

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Drones have been studied in a variety of industries. Drone detection is one of the most important task. The goal of this paper is to detect the target drone using the microphone and a camera of the detecting drone by training deep learning models. For evaluation, three methods are used: visual-based, audio-based, and the decision fusion of both features. Image and audio data were collected from the detecting drone, by flying two drones in the sky at a fixed distance of 20m. CNN (Convolutional Neural Network) was used for audio, and YOLOv5 was used for computer vision. From the result, the decision fusion of audio and vision-based features showed the highest accuracy among the three evaluation methods.

引用

页码：91 / 92

页数：2

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