![](bg8.png)
Christnacher, F., Hengy, S., Laurenzis, M., Matwyschuk,
A., Naz, P., Schertzer, S., & Schmitt, G. (2016,
October). Optical and acoustical UAV detection. In
Electro-Optical Remote Sensing X (Vol. 9988, pp. 83-
95). SPIE.
Deng, S., Li, S., Xie, K., Song, W., Liao, X., Hao, A., &
Qin, H. (2020). A global-local self-adaptive network
for drone-view object detection. IEEE Transactions on
Image Processing, 30, 1556-1569.
Dong, Qiushi, Yu Liu, and Xiaolin Liu. ”Drone sound de-
tection system based on feature result-level fusion us-
ing deep learning.” Multimedia Tools and Applica-
tions (2022): 1-23.
Feng, Tuo, and Dongbing Gu. ”SGANVO: Unsuper-
vised deep visual odometry and depth estimation
with stacked generative adversarial networks.”IEEE
Robotics and Automation Letters 4.4 (2019): 4431-
4437.
H. Abdullah, ”Man Detained for Flying Drone Near
White House”. NEWS, May. 15, 2015. [On-
line]. Available: https://www.nbcnews.com/news/us-
20news/20man-20detained-20trying- 20fly-20drone-
20near-20white-20house-20n359011
H. Salloum, A. Sedunov, N. Sedunov, A. Sutin and D. Mas-
ters, J. Salamon and J. P. Bello, ”Deep Convolutional
Neural Networks and Data Augmentation for Environ-
mental Sound Classification,” in IEEE Signal Process-
ing Letters, vol. 24, no. 3, pp. 279-283, March 2017,
doi: 10.1109/LSP.2017.2657381.
H. Liu, Z. Wei, Y. Chen, J. Pan, L. Lin and Y. Ren, ”Drone
Detection Based on an Audio-Assisted Camera Ar-
ray,” 2017 IEEE Third International Conference on
Multimedia Big Data (BigMM), 2017, pp. 402-406,
doi: 10.1109/BigMM.2017.57.
Hu, Y., Wu, X., Zheng, G., & Liu, X. (2019, July). Ob-
ject detection of UAV for anti-UAV based on im-
proved YOLO v3. In 2019 Chinese Control Confer-
ence (CCC) (pp. 8386-8390). IEEE.
Heartexlabs, ”labelImg”, github.com (2014)
Jeon, S., Shin, J. W., Lee, Y. J., Kim, W. H., Kwon, Y.,
& Yang, H. Y. (2017, August). Empirical study of
drone sound detection in real-life environment with
deep neural networks. In 2017 25th European Signal
Processing Conference (EUSIPCO) (pp. 1858-1862).
IEEE.
Kim, J., Lee, D., Kim, Y., Shin, H., Heo, Y., Wang, Y., &
Matson, E. T. (2022). Deep Learning Based Malicious
Drone Detection Using Acoustic and Image Data (No.
9335). EasyChair.
Madasamy, K., Shanmuganathan, V., Kandasamy, V., Lee,
M. Y., & Thangadurai, M. (2021). OSDDY: embed-
ded system-based object surveillance detection system
with small drone using deep YOLO. EURASIP Jour-
nal on Image and Video Processing, 2021(1), 1-14.
S. Al-Emadi, A. Al-Ali, A. Mohammad and A. Al-
Ali, ”Audio Based Drone Detection and Identifi-
cation using Deep Learning,” 2019 15th Interna-
tional Wireless Communications & Mobile Comput-
ing Conference (IWCMC), 2019, pp. 459-464, doi:
10.1109/IWCMC.2019.8766732.
Sedunov, A., Sutin, A., Sedunov, N., Salloum, H.,
Yakubovskiy, A., & Masters, D. (2016). Passive
acoustic system for tracking low-flying aircraft. IET
Radar, Sonar & Navigation, 10(9), 1561-1568.
S. Jeon, J. -W. Shin, Y. -J. Lee, W. -H. Kim, Y. Kwon and H.
-Y. Yang, ”Empirical study of drone sound detection
in real-life environment with deep neural networks,”
2017 25th European Signal Processing Conference
(EUSIPCO), 2017, pp. 1858-1862, doi: 10.23919/EU-
SIPCO.2017.8081531.
S. Seo, S. Yeo, H. Han, Y. Ko, K. E. Ho and E. T. Matson,
”Single Node Detection on Direction of Approach,”
2020 IEEE International Instrumentation and Mea-
surement Technology Conference (I2MTC), 2020, pp.
1-6, doi: 10.1109/I2MTC43012.2020.9129016.
Seo, Y., Jang, B., & Im, S. (2018, November). Drone detec-
tion using convolutional neural networks with acous-
tic STFT features. In 2018 15th IEEE International
Conference on Advanced Video and Signal Based
Surveillance (AVSS) (pp. 1-6). IEEE.
Ultralytics, ”YOLOV5”, github.com
https://github.com/ultralytics/YOLOV5
Wang, C. Y., Liao, H. Y. M., Wu, Y. H., Chen, P. Y., Hsieh,
J. W., & Yeh, I. H. (2020). CSPNet: A new backbone
that can enhance learning capability of CNN. In Pro-
ceedings of the IEEE/CVF conference on computer
vision and pattern recognition workshops (pp. 390-
391).
Winters-Hilt, S., Yelundur, A., McChesney, C., & Landry,
M. (2006, September). Support vector machine im-
plementations for classification & clustering. In BMC
bioinformatics (Vol. 7, No. 2, pp. 1-18). BioMed Cen-
tral.
Wu, Z., Wu, X., Zhang, X., Wang, S., & Ju, L. (2019). Spa-
tial correspondence with generative adversarial net-
work: Learning depth from monocular videos. In Pro-
ceedings of the IEEE/CVF International Conference
on Computer Vision (pp. 7494-7504).
Y. Wang, F. E. Fagian, K. E. Ho and E. T. Matson, ”A Fea-
ture Engineering Focused System for Acoustic UAV
Detection,” 2021 Fifth IEEE International Conference
on Robotic Computing (IRC), 2021, pp. 125-130, doi:
10.1109/IRC52146.2021.00031.
Yip, D. A., Knight, E. C., Haave-Audet, E., Wilson, S. J.,
Charchuk, C., Scott, C. D., ... & Bayne, E. M. (2020).
Sound level measurements from audio recordings pro-
vide objective distance estimates for distance sam-
pling wildlife populations. Remote Sensing in Ecol-
ogy and Conservation, 6(3), 301-315.
ICAART 2023 - 15th International Conference on Agents and Artificial Intelligence
884