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CNN-based UAV detection with short time fourier transformed acoustic features
- Kim B.;
- Jang B.;
- Lee D.;
- Im S.
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8초록
In this study, CNN (Convolutional Neural Network) is applied to the UAV detection, which is expanding its application in various fields, to compare the detection performance of the UAV against the noise of a small fan and the drum. In this study, the drum sound and the small fan sound are collected and compared with the UAV's hovering acoustic signal data. We evaluate the detection performance by using CNN for the features obtained by applying short-time Fourier transform to the samples. In the experiment, the UAV detection rate against the acoustic signal of the small fan is 99.74 % and the false detection rate is 0.39 %. For the drum sound, the detection rate is 99.98 % and the false detection rate is 0.20 %. © 2020 IEEE.
키워드
CNN; Deep Learning; Detection; STFT; UAV; Acoustic waves; Convolutional neural networks; Feature extraction; Unmanned aerial vehicles (UAV); Acoustic features; Acoustic signals; Detection performance; Detection rates; False detections; ITS applications; Short time Fourier transforms; Small fans; Aircraft detection
- 제목
- CNN-based UAV detection with short time fourier transformed acoustic features
- 저자
- Kim B.; Jang B.; Lee D.; Im S.
- 발행일
- 2020-04
- 유형
- Conference Paper
- 저널명
- 2020 International Conference on Electronics, Information, and Communication, ICEIC 2020
- 언어
- ENG
- 출판사
- Institute of Electrical and Electronics Engineers Inc.
- 발행국가
- 미국
- ISSN
- P 0000-0000