Intelligent Surveillance Tower for Detection of the Drone from the Other Aerial Objects Using Deep Learning
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-6984-5_3
Reference7 articles.
1. Wu, M., Xie, W., Shi, X., Shao, P., & Shi, Z. (2018). Real-time drone detection using deep learning approach. In L. Meng, & Y. Zhang (Eds.), Machine learning and intelligent communications. MLICOM 2018. Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering (vol. 251). Springer. https://doi.org/10.1007/978-3-030-00557-3_3
2. Lee, D., Gyu La, W., & Kim, H. (2018). Drone detection and identification system using artificial intelligence. In 2018 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 1131–1133). https://doi.org/10.1109/ICTC.2018.8539442
3. Hu, Y., Wu, X., Zheng, G., & Liu, X. (2019). Object detection of UAV for anti-UAV based on improved YOLO v3. In 2019 Chinese control conference (CCC) (pp. 83868390). https://doi.org/10.23919/ChiCC.2019.8865525
4. Madasamy, K., Shanmuganathan, V., Kandasamy, V., et al. (2021). OSDDY: Embedded systembased object surveillance detection system with small drone using deep YOLO. Journal on Image and Video Processing, 2021, 19. https://doi.org/10.1186/s13640-021-00559-1
5. Hassan, S. A., Rahim, T., & Shin, S. Y. (2019). Real-time UAV detection based on deep learning network. International Conference on Information and Communication Technology Convergence (ICTC), 2019, 630–632. https://doi.org/10.1109/ICTC46691.2019.8939564
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