Autonomous long-range drone detection system for critical infrastructure safety

Xindi Zhang,Kusrini Kusrini

doi:10.1007/s11042-020-10231-x

Abstract

The 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%.

Highlights

Unmanned aerial vehicles (UAV), known as drones, have developed rapidly in recent years
To address the challenge of detecting small objects taken from a distance, the use of deep learning models has been reported in the literature, such as AZNet [20], TridentNet [16], SNIPER [25]
The system presents a seamless operation between the deep-learning algorithm signalling low-cost hardware

Summary

Introduction

Unmanned aerial vehicles (UAV), known as drones, have developed rapidly in recent years. Nowadays, companies such as Amazon, Alibaba, and even pizza chains are pushing forward to use drones, for service provision such as package and food delivery. To address the challenge of detecting small objects taken from a distance, the use of deep learning models has been reported in the literature, such as AZNet [20], TridentNet [16], SNIPER [25]. A framework of drone detection and tracking has been proposed in [31], which can run in real-time. This paper proposes a dual camera system that combines traditional computer vision algorithms and deep learning algorithms to achieve real-time long-range UAV detection and tracking

Methods

Findings

Discussion

Conclusion