Aerial Supervision of Drones and Other Flying Objects Using Convolutional Neural Networks-Reference-Cited by-同舟云学术

Aerial Supervision of Drones and Other Flying Objects Using Convolutional Neural Networks

Published:2022-02-21 Issue: Volume: Page:
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Container-title:2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
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Author:

Ihekoronye Vivian Ukamaka¹,Ajakwe Simeon Okechukwu¹,Kim Dong-Seong¹,Lee Jae Min¹

Affiliation:

1. Kumoh National Institute of Technology,Department of IT Convergence Engineering,Gumi,South Korea

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9722611/9722613/09722702.pdf?arnumber=9722702

Reference14 articles.

1. Anti-drone systems design: Safeguarding airspace through real-time trustworthy ai paradigm;ajakwe;The 2nd Korea Artificial Intelligence Conference (KAIC) 09,2021

2. Drone detection using YOLOv3 with transfer learning on NVIDIA Jetson TX2;xun;2021 Second International Symposium on Instrumentation Control Artificial Intelligence and Robotics (ICA-SYMP),2021

3. Real-time UAV Detection based on Deep Learning Network

4. SPB-YOLO: An Efficient Real-Time Detector For Unmanned Aerial Vehicle Images

5. UAV detection based on rainy environment

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