Drone Detection Using Image Processing Based on Deep Learning
Keywords:
CFD, modeling, simulation, car brake, cooling
Abstract
The objective of this experimental research is to identify solutions to detect drones using computer vision algorithm. Nowadays danger of drones operating near airports and other important sites is of utmost importance. The proposed techniques resolution pictures with a good rate of detection. The technique is using information concerning movement patterns of drones.
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References
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[2]. ***, https://news.in-24.com/news/186066.html, 2021.
[3]. ***, Regierungschef übersteht Drohnenangriff, https://www-tagesschau.de.
[4]. Krizhevsky A., Sutskever I., Hinton G. E., Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, p. 1097-1105, 2012.
[5]. Shengxiang Qi W. Z., Detecting Consumer Drones from Static Infrared Images, Proceedings of the 4th International Conference on Communication and Information Processing, p. 62-66, Qingdao, 2018.
[6]. Stokel-Walker Chris, Why Drones Cause Airport Chaos, New Scientist 241, no. 3213, 10, https://doi.org/10.1016/S0262-4079(19)30100-9, January 19, 2019.
[7]. Huang K., Wang H., Combating the control signal spoofing attacking UAV systems, IEEE Transactions on Vehicular Technology, vol. 67, no. 8, p. 7769-7773, Aug. 2018.
[8]. Zeitlin A. D., Sense avoids capability development challenges, IEEE Aerospace and Electronic Systems Magazine, vol. 25, no. 10, p. 27-32, Oct. 2010.
[9]. Nam H., Han B., Learning multi-domain convolutional neural networks for visual tracking, arXiv preprint arXiv:1510.07945, 2015.
[10]. Redmon J., Divvala S., Girshick R., Farhadi A., You only look once: Unified, real-time object detection, arXiv preprint arXiv:1506.02640, 2015.
[11]. Opromolla R., Fasano G., Accardo D., Perspectives and sensing concepts for small uas sense and avoid, IEEE/AIAA 37th Digital Avionics Systems Conference (DASC), p. 1-10, Sep. 2018.
[12]. Prats X., Delgado L., Ramirez J., Royo P., Pastor E., Requirements, issues, and challenges for sense and avoid in unmanned aircraft systems, Journal of Aircraft, vol. 49, no. 3, p. 677-687, 2012.
[13]. Yucong Lin, Saripalli S., Sense and avoid for unmanned aerial vehicles using ads-b, IEEE International Conference on Robotics and Automation (ICRA), p. 6402-6407, May 2015.
Published
2021-12-15
How to Cite
1.
MARIN F-B, MARIN M. Drone Detection Using Image Processing Based on Deep Learning. The Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science [Internet]. 15Dec.2021 [cited 3Dec.2024];44(4):36-9. Available from: https://gup.ugal.ro/ugaljournals/index.php/mms/article/view/4974
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