Drone Detector

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.