Autonomous long-range drone detection system for critical infrastructure safety

Abstract

AbstractThe development of unmanned aerial vehicles has been identified as a potential source of a weapon for causing operational disruptions against critical infrastructures. To mitigate and neutralise the threat posed by the misuse of drones against malicious and terrorist activity, this paper presents a holistic design of a long-range autonomous drone detection platform. The novelty of the proposed system lies in the confluence between the design of hardware and software components to effective and efficient localisation of the intruder objects. The research presented in the paper proposes the design and validation of a situation awareness component which is interfaced with the hardware component for controlling the focal length of the camera. The continuous stream of media data obtained from the region of vulnerability is processed using the object detection that is built on region based fully connected neural network. The novelty of the proposed system relies on the processing of multi-threaded dual-media input streams that are evaluated to mitigate the latency of the system. Upon the successful detection of malicious drones, the system logs the occurrence of intruders that consists of both event description and the associated media evidence for the deployment of the mitigation strategy. The analytics platform that controls the signalling of the low-cost sensing equipment contains the NVIDIA GeForce GTX 1080 for detecting drones. The experimental testbeds developed for the validation of the proposed system has been constructed to include environments and situations that are commonly faced by critical infrastructure operators such as the area of protection, drone flight path, tradeoff between the angle of coverage against the distance of coverage. The validation of the proposed system has resulted in yielding a range of intruder drone detection by 250m with an accuracy of 95.5%.