Abstract:
Advances in unmanned aerial vehicle (UAV) technology have made UAVs available to
the general public. Drones can be operated autonomously under remote control
human operators or by onboard computers. Compared to manned aircraft, drones are easier to
operate and can be operated from anywhere. Drones will be extensively used in the smart cities
of the future for the wireless methodology, delivering goods, and for preserving the safety of
smart cities. However, recent world events have shown that the rapid increase in the number
of UAVs, poses threats to privacy and security. Therefore, it is important to think about how to
prevent UAV threats to protect our privacy and security.
One of the main challenges in recognizing aerial objects using computer vision is
distinguishing other flying objects from long‐range targets.
In this study, to overcome the limitations of other methods of drone detection we
propose an amenable framework to detect malicious drone and ensure public safety using
image processing techniques. The proposed model is validated using 3000 images with various
challenges such as obstruction, scale distinction, haziness, background chaos, and low light.
