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Drone Tracking with Drone Using Deep Learning


Affiliations
1 Computer Engineering Department, Firat University, Elazig, Turkey
2 Erzincan Binali Yıldırım University, Erzincan, Turkey
 

With the development of technology, studies in fields such as artificial intelligence, computer vision and deep learning are increasing day by day. In line with these developments, object tracking and object detection studies have spread over wide areas. In this article, a study is presented by simulating two different drones, a leader and a follower drone, accompanied by deep learning algorithms. Within the scope of this study, it is aimed to perform a drone tracking with drone in an autonomous way. Two different approaches are developed and tested in the simulator environment within the scope of drone tracking. The first of these approaches is to enable the leader drone to detect the target drone by using object-tracking algorithms. YOLOv5 deep learning algorithm is preferred for object detection. A data set of approximately 2500 images was created for training the YOLOv5 algorithm. The Yolov5 object detection algorithm, which was trained with the created data set, reached a success rate of approximately 93% as a result of the training. As the second approach, the object-tracking algorithm we developed is used. Trainings were carried out in the simulator created in the Matlab environment. The results are presented in detail in the following sections. In this article, some artificial neural networks and some object tracking methods used in the literature are explained.

Keywords

Unmanned Aerial Vehicle, Drone Tracking, Deep Learning, Yolov5, Object Detection.
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  • J. Kim, S. Kim, C. Ju and H. I. Son, "Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications," IEEE Access, vol. 7, pp. 105100 - 105115, 2019.

  • P. Gupta, B. Pareek, G. Singal and D. V. Rao, "Edge device based Military Vehicle Detection and Classification from UAV," Multimedia Tools and Applications, pp. 1-22, 2021.

  • Y. Lin, T. Wang and S. Wang, «UAV-Assisted Emergency Communications: An Extended Multi-Armed Bandit Perspective,» IEEE Communications Letters, vol. 23, no. 5, pp. 938 - 941, 2019.

  • M. A. Akhloufi, S. Arola and A. Bonnet, «Drones Chasing Drones: Reinforcement learning and deep search area proposal,» Drones, vol. 3, no. 3, p. 58, 2019.

  • S. Dogru, R. Baptista and L. Marques, "Tracking Drones with Drones Using Millimeter Wave Radar," Fourth Iberian Robotics Conference, pp. 392-402, 2020.

  • 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 Transactions on Computer Vision and Applications , vol. 11, no. 1, pp. 1-13, 2019.

  • P. M. Wyder, Y.-S. Chen, A. J. Lasrado, R. J. Pelles and R. Kwiatkowski, «Autonomous drone hunter operating by deep learning and all-onboard computations in GPS-denied environments,» PloS one, vol. 14, no. 11, 2019.

  • Z. Tan and M. Karaköse, "On-Policy Deep Reinforcement Learning Approach to Multi Agent Problems," in In Interdisciplinary Research in Technology and Management , Kolkata, 2021.

  • J. Hu, H. Zhang, L. Song, R. Schober and H. V. Poor, «Cooperative internet of UAVs: Distributed trajectory design by multi-agent deep reinforcement learning.,» IEEE Transactions on Communications, vol. 68, no. 11, pp. 6807-6821, 2020.

  • W. J. Yuna, S. Junga, J. Kima and J.-H. Kimb, «Distributed deep reinforcement learning for autonomous aerial eVTOL mobility in drone taxi applications,» ICT Express, vol. 7, no. 1, pp. 1-4, 2021.

  • Z. Tan and M. Karaköse, "Proximal Policy Based Deep Reinforcement Learning Approach for Swarm Robots," in 2021 Zooming Innovation in Consumer Technologies Conference (ZINC), Novi Sad, 2021.

  • H. Cheng, L. Bertizzolo, S. D’Oro, J. Buczek, T. Melodia and E. S. Bentley, «Learning to Fly: A Distributed Deep Reinforcement Learning Framework for Software-Defined UAV Network Control,» in IEEE Open Journal of the Communications Society, 2021.

  • F. Venturini, “Distributed Deep Reinforcement Learning for Drone Swarm Control”, University of Padova, 2019.

  • R. N. Haksar and M. Schwager, «Distributed Deep Reinforcement Learning for Fighting Forest Fires with a Network of Aerial Robots» 018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1067-1074, Madrid, 2018.

  • S. Park, H. T. Kim, S. Lee, H. Joo And H. Kim, «Survey on Anti-Drone Systems: Components,Designs, and Challenges,» IEEE Access, vol. 9, pp. 42635 - 42659, 2021.

  • E. Çetin, C. Barrado, G. Munoz, M. Macias and E. Papaz, «Drone Navigation and Avoidance of Obstacles Through Deep Reinforcement Learning,» 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC), pp. 1-7, San Diego, 2019.

  • Z. Tan and M. Karaköse, «Comparative Evaluation for Effectiveness Analysis of Policy Based Deep Reinforcement Learning Approaches,» International Journal of Computer and Information Technology, vol. 10, no. 3, 2021.

  • P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, vol. 1, Kauai , 2001.

  • Bavarian, B.«Introduction to neural networks for intelligent control,» IEEE Control Systems Magazine, vol. 8, no. 2, pp. 3-7, 1988.

  • R. Chauhan, K. K. Ghanshala and R. Joshi, "Convolutional Neural Network (CNN) for Image Detection and Recognition," in 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), Jalandhar, India, 2019.

  • R. Girshick, «Fast r-cnn,» Proceedings of the IEEE international conference on computer vision, pp. 1440-1448, 2015.

  • J. Redmon, S. Divvala, R. Girshick and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779-788, Las Vegas, 2016.

  • T. Kong, A. Yao, Y. Chen and F. Sun, "HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 2016.

  • S. Liu, Q. Lu, H. Qin, J. Shi and J. Jia, "Path Aggregation Network for Instance Segmentation," Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8759-8768, Salt Lake City, 2018.

  • Y. Liu, BH. Lu, J. Peng, Z. Zhang, "Research on the Use of YOLOv5 Object Detection Algorithm in Mask Wearing Recognition," World Scientific Research Journal, vol. 6, no. 11, pp. 276 - 284, 2020.

  • F. Farahi and H. S. Yazdi, "Probabilistic Kalman filter for moving object tracking," Signal Processing: Image Communication, vol. 82, 2020.

  • P. Djuric, J. Kotecha, J. Zhang, Y. Huang, T. Ghirmai, M. Bugallo and J. Miguez, " IEEE Signal Processing Magazine," IEEE Signal Processing Magazine, vol. 20, no. 5, pp. 19-38, 2003.

  • A. Nagy and V. Savona, "Variational quantum Monte Carlo method with a neural-network ansatz for open quantum systems," Physical review letters, vol. 122, no. 25, 2019.

  • T. Han, L. Wang and B. Wen, "The kernel based multiple instances learning algorithm for object tracking," Electronics, vol. 7, no. 6, pp. 97-100, 2018.

  • R. Yamasaki and T. Tanaka, "Properties of Mean Shift," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 9, pp. 2273 - 2286, 2019.

  • K. Ragland and P. Tharcis, "A survey on object detection, classification and tracking methods," Int. J. Eng. Res. Technol, vol. 3, no. 11, pp. 622-628, 2014.


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  • Drone Tracking with Drone Using Deep Learning

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Authors

Mehmet KARAKÖSE
Computer Engineering Department, Firat University, Elazig, Turkey
Ziya TAN
Erzincan Binali Yıldırım University, Erzincan, Turkey
Elif ÖZET
Computer Engineering Department, Firat University, Elazig, Turkey

Abstract


With the development of technology, studies in fields such as artificial intelligence, computer vision and deep learning are increasing day by day. In line with these developments, object tracking and object detection studies have spread over wide areas. In this article, a study is presented by simulating two different drones, a leader and a follower drone, accompanied by deep learning algorithms. Within the scope of this study, it is aimed to perform a drone tracking with drone in an autonomous way. Two different approaches are developed and tested in the simulator environment within the scope of drone tracking. The first of these approaches is to enable the leader drone to detect the target drone by using object-tracking algorithms. YOLOv5 deep learning algorithm is preferred for object detection. A data set of approximately 2500 images was created for training the YOLOv5 algorithm. The Yolov5 object detection algorithm, which was trained with the created data set, reached a success rate of approximately 93% as a result of the training. As the second approach, the object-tracking algorithm we developed is used. Trainings were carried out in the simulator created in the Matlab environment. The results are presented in detail in the following sections. In this article, some artificial neural networks and some object tracking methods used in the literature are explained.

Keywords


Unmanned Aerial Vehicle, Drone Tracking, Deep Learning, Yolov5, Object Detection.

References