[1] K. Abbasi, A. Batool, M.A. Asghar, A. Saeed, M.J. Khan and M. ur Rehman, A vision-based amateur drone
detection algorithm for public safety applications, 2019 UK/China Emerg. Technol. UCET 2019 (2019), 1–5.
[2] Y. He, I. Ahmad, L. Shi and KH. Chang, SVM-based drone sound recognition using the combination of HLA and
WPT techniques in practical noisy environment, KSII Trans. Internet Inf. Syst. 13 (2019), no. 10, 5078–5094.
[3] M.A. Akhloufi, S. Arola and A. Bonnet, Drones chasing drones: reinforcement learning and deep search area
proposal, Drones 3 (2019), no. 3, 1–14.
[4] M.S. Allahham, M.F. Al-Sa’d, A. Al-Ali, A. Mohamed, T. Khattab and A. Erbad, DroneRF dataset: a dataset
of drones for RF-based detection, classification and identification, Data Br. 26 (2019).
[5] A. Bochkovskiy, C.-Y. Wang and H.-Y.M. Liao, YOLOv4: optimal speed and accuracy of object detection, arXiv
preprint arXiv:2004.10934. (2020).
[6] E. Burger and G. Bordacchini, Global space policies and programmes, In Yearbook on Space Policy, Springer,
Cham. 2019.
[7] Caltech Vision Lab, C.-U. Birds-200-2011, http://www.vision.caltech.edu/visipedia/CUB-200-2011.html, (2011).
[8] A. Coates, H. Lee and A.Y. Ng, An analysis of single layer networks in unsupervised feature learning, Proc. 14th
Int. Conf. Artific. Intell. Statist. (AISTATS), Fort Lauderdale, FL, USA, 2011.
[9] A. Coluccia, A. Fascista, A. Schumann, L. Sommer, A. Dimou, D. Zarpalas, F.C. Akyon, O. Eryuksel, K.A.
Ozfuttu, S.O. Altinuc and F. Dadboud, Drone-vs-bird detection challenge at IEEE AVSS2019, 16th IEEE Int.
Conf. Adv. Video Signal Based Surveillance, AVSS, 2019, pp. 1–8.
[10] A.R. Eldosouky, A. Ferdowsi and W. Saad, Drones in distress: A game-theoretic countermeasure for protecting
UAVs against GPS spoofing, IEEE Internet Things J. 7 (2020), no. 4, 2840–2854.
[11] J. Gao, C.-D. L¨u, Y.-L. Shen, Y.-M. Wang and Y.-B. Wei, Precision calculations of B −→ V form factors in
QCD, arXiv preprint arXiv:1907.11092. (2019).
[12] S. Gr´ac, P. Beˇno, F. Duchoˇn, M. Dekan and M. T¨olgyessy, ˇ Automated detection of multi-rotor UAVs using a
machine-learning approach, Appl. Syst. Innov. 3 (2020), no. 3, 1–23.
[13] D. Kinaneva, G. Hristov, J. Raychev and P. Zahariev, Early forest fire detection using drones and artificial
intelligence, 42nd Int. Conv. Inf. Commun. Technol. Electron. Microelectron. MIPRO 2019 Proc., 2019, pp.
1060–1065.
[14] S. Marathe, Leveraging drone based imaging technology for pipeline and RoU monitoring survey, Soc. Pet. Eng. -
SPE Symp. Asia Pacific Heal. Safety, Secur. Environ. Soc. Responsib. 2019 (2019).
[15] S. Mayer, L. Lischke and P.W. Wo´zniak, Drones for search and rescue, 1st Int. Workshop on Human-Drone
Interaction, 2019.
[16] D. Misra, Mish: a self regularized non-monotonic activation function, arXiv preprint arXiv:1908.08681. (2019).
[17] PASCAL 2, Visual Object Classes Challenge 2011 (VOC2011), http://host.robots.ox.ac.uk:8080/pascal/VOC/voc2011/inde2011.
[18] M. Pawe lczyk and M. Wojtyra, Real world object detection dataset for quadcopter unmanned aerial vehicle detection, IEEE Access 8 (2020), 174394–174409.
[19] T. Preethi Latha, K. Naga Sundari, S. Cherukuri and M.V.V.S.V. Prasad, Remote sensing uav/drone technology
as a tool for urban development measures in apcrda, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS
Arch. 42 (2019), no. 2/W13, 525–529.[20] J. Redmon and A. Farhadi, YOLOv3: an incremental improvement, arXiv preprint arXiv:1804.02767. (2018).
[21] Q Shi and, J Li , Object detection of UAV for anti-UAV based on YOLOv4, IEEE 2nd Int. Conf. Civil Aviation
Safety Inf. Technol.(ICCASIT. IEEE, 2020, pp. 1048–1052.
[22] S. Singha and B. Aydin, Automated drone detection using YOLOv4, Drones 5 (2021), no. 3.
[23] D. Tezza and M. Andujar, The state-of-the-art of human-drone interaction: A survey, IEEE Access 7 (2019),
167438–167454.
[24] E. Unlu, E. Zenou, N. Riviere and P.E. Dupouy, Deep learning-based strategies for the detection and tracking of
drones using several cameras, IPSJ Trans. Comput. Vis. Appl. 11 (2019), no. 1.
[25] E. Unlu, E. Zenou, N. Riviere and P.E. Dupouy, An autonomous drone surveillance and tracking architecture,
Electronic Imag. 2019 (2019), no. 15, 1–35.
[26] J.P. Winkler, J. Gr¨onberg and A. Vogelsang, Optimizing for recall in automatic requirements classification: an
empirical study, IEEE 27th Int. Requir. Engin. Conf., IEEE, 2019, pp. 40–50.
[27] M. Wu, W. Xie, X. Shi, P. Shao and Z. Shi, Real-time drone detection using deep learning approach, Lect. Notes
Inst. Comput. Sci. Soc. Telecommun. Eng. LNICST 251 (2018), 22–32.
[28] J. Xu, L. Guo, J. Jiang, B. Ge and M. Li, A deep learning methodology for automatic extraction and discovery of
technical intelligence, Technol. Forecast. Soc. Change 146 (2019), 339–351.
[29] Z. Zhang, T. He, H. Zhang, Z. Zhang, J. Xie and M. Li, Bag of freebies for training object detection neural
networks, arXiv preprint arXiv:1902.04103. (2019).