A REVIEW ON IMPROVEMENT OF DRONE DETECTION AND TRACKING SYSTEM USING ARTIFICIAL INTELLIGENCE
Keywords:
Drone safety, Drone security, artificial intelligence,UAV etc.Abstract
The development of the drone industry has led to an increase injudicious, unauthorized, and illegal drone use, which has resulted in significant harm to society and the economy. They look at some major incidents involving drones around the world and identify essential characteristics for upcoming anti-drone systems. According to this study, the drone industry has made drones that can be used in everyday life available to the general public because of the widespread interest in them. However, as the use of drones became more widely available, the likelihood of accidents has increased, raising concerns regarding safety and security: slipping out of control, colliding with others, or breaking into secured properties. It is fundamental for the two eyewitnesses and the robot to know about an oncoming robot for wellbeing reasons. This paper presents a comprehensive drone detection system based on machine learning as a result of a literature review of various studies and the various issues encountered. All of these issues will be addressed by future research.
References
H. A. Mwenegoha, T. Moore, J. Pinchin and M. Jabbal, 2020. "A Model-based Tightly Coupled Architecture for Low-Cost Unmanned Aerial Vehicles for Real-Time Applications," in IEEE Access.
Hamatapa, R. and Vongchumyen, C., (2019)”Image processing for drones detection” In 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST) (pp. 1-4).
Lee, D., La, W.G. and 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).
Mahdavi, F. and Rajabi, R., (2020) “December. Drone detection using convolutional neural networks” In 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) (pp. 1-5).
MARIN, F.B. and MARIN, M., (2021) “Drone detection using image processing based on deep learning” The Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science, 44(4), pp.36-39.
Pham, G.N. and Nguyen, P.H., (2020) “Drone detection experiment based on image processing and machine learning” International Journal of Scientific & Technology Research, 9(2), pp.2965-2971.
Ramachandran, A. and Sangaiah, A.K., (2021) “A review on object detection in unmanned aerial vehicle surveillance” International Journal of Cognitive Computing in Engineering, 2, pp.215-228.
S. Karlsson, C. Kanellakis, S. S. Mansouri and G. Nikolakopoulos, 2021. "Monocular Vision-based Obstacle Avoidance Scheme for Micro Aerial Vehicle Navigation," International Conference on Unmanned Aircraft Systems (ICUAS),pp.1321-1327.
Trapal, D.D.C., Leong, B.C.C., Ng, H.W., Zhong, J.T.G., Srigrarom, S. and Chan, T.H., (2021) “Improvement of vision-based drone detection and tracking by removing cluttered background, shadow and water reflection with super resolution” In 6th International Conference on Control and Robotics Engineering (ICCRE) (pp. 162-168).
