Drone Detection and Classification Using YOLOv8 and Deep CNN
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-69197-3_2
Reference20 articles.
1. Alam, S.S., et al.: RF-enabled deep-learning-assisted drone detection and ıdentification: an end-to-end approach. Sensors 23(9), 4202 (2023). https://doi.org/10.3390/s23094202
2. Kumar, D.B.S., Varma, V.A., Rani, S.D., Nishanth, A.: Fault detection and classification using machine learning. Int. J. Res. Appl. Sci. Eng. Technol. 11(4), 3063–3069 (2023). https://doi.org/10.22214/ijraset.2023.50867
3. Aydin, B., Singha, S.: Drone detection using YOLOv5. Eng 4(1), 416–433 (2023). https://doi.org/10.3390/eng4010025
4. Yasmine, G., Maha, G., Hicham, M.: Anti-drone systems: an attention based improved YOLOv7 model for a real-time detection and identification of multi-airborne target. Intell. Syst. Appl. 20, 200296 (2023). https://doi.org/10.1016/j.iswa.2023.200296
5. Al Dawasari, H.J., Bilal, M., Moinuddin, M., Arshad, K., Assaleh, K.: Pre-trained deep learning networks for advanced visible ımagery drone detection and recognition. In: Proceedings - 2023 15th IEEE International Conference Computer Intelligent Communication Networks, CICN 2023, pp. 316–320 (2023). https://doi.org/10.1109/CICN59264.2023.10402291
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