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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 d...
Citations
... The integration of diverse sensors into a multi-hypothesis tracker system showcases the flexibility of passive sensor processing for drone detection. Furthermore, the studies by Svanstrom et al. [4], Fung et al. [6], and Liu et al. [7] reinforce the importance of multisensor approaches, integrating visible, thermal, acoustic, and audio sensors for drone detection. They investigate the impact of sensor-to-target distance on performance, provide annotated datasets, and discuss the advantages of sensor fusion in reducing false positives and improving overall system robustness. ...
... 176 If a nation's security is ensured, it can pursue other purposes, including welfare initiatives. 177 However, where a government lacks faith and trust in its neighbours' intentions, this can affect its attitude towards purchasing and acquiring armaments. 178 To ensure their own security, nations strive to increase and modernise their weaponry for offensive and defensive means. ...
... Internet drone applied for profitable condition develop extra prevalent when Amazon broadcast it means to apply drones for providing waste materials detection of images in smart cities. This is a so charming and life-changing notion with numerous planned and unplanned values [2]. ...
... In this paper denote this issue waste materials detection of images in smart cities using Internet drone, which uses the Internet drone to detention images of waste materials in residences. To let know It send the detection of images and site of that area to adjacent waste group authority using Global Positioning System (GPS) and Global System for Mobile (GSM) segment [2,8]. ...
Internet drone, in modest things, is a kind of rapid automaton that is remotely organized by human beings. It is in machinery relations also known as Unmanned Floating Automobile (UFA). By numerous innovative developments in drone equipment, it has predictable that drones grow additional to contain analytical skills, specifically in the waste materials detection of images in smart cities. The drone offered in this article will be applied for the distribution of waste materials detection of images. Through a security system executed on the vessel involved, it confirms the protection of the waste materials detection of images till they get the correct place. The planned structure is an internet-based drone consuming the values of Internet of Things. Still, the procedure of drones’ desires trained control and appropriate setup. Waste materials detection of images by internet drone are misguidedly measured as security drones, therefore has been criticized by prepared services.
... Object detection is one of the most important and challenging tasks in computer vision and has received much attention in recent years for its applications in surveillance [32,42] and autonomous driving [28,43]. Object detection can be divided into two-stage and onestage categories. ...
Region proposal is crucial for the two-stage object detectors. Recently, the RGB-based region proposal approaches have achieved impressive progress. However, they still suffer from two problems: (1) RGB images only contain the texture information of objects, while the 3D geometric structure information which is also important for detection is neglected. (2) in a typical Feature Pyramid Network (FPN), the upsampling operation only models the corresponding relation between adjacent locations, the texture structure is not taken into consideration. Besides, the addition operation in FPN ignores the importance of different channels which may affect the propagation of semantic information. In this paper, we propose a Region Proposal Network using Multi-modalities and multi-scales Information (named MI-RPN). Firstly, we propose a Gate-guided Fusion Module (GFM) to fuse the RGB and depth features which respectively contain the texture and geometric information. Secondly, we propose a Flow-guided Upsample Feature Pyramid Network (FUFPN) to optimize the multi-scales feature fusion in typical FPN by taking features of an adjacent layer into consideration. Experimental results on SUNRGBD, NYUv2, and KITTI show that MI-RPN achieves superior results compared to current state-of-the-art methods. Besides, we replace the RPN in typical two-stage object detection models to test the effectiveness of the proposed MI-RPN. The results show that MI-RPN can significantly improve the accuracy of two-stage object detection models.
... Figures 1 and 2 cover the block diagram of the internet drone with the securing tool applied for the container. As internet drone procedure takes off, it's only a material of period earlier they detect their place in waste material [5]. It previously be seen ying over land lls and cropland, observing waste, and taking images in smart cities shown in Fig. 1. ...
... [10] It provides the modernizes of the waste containers to the permitted consumers and in this way distributes with the requirement of unbalanced physical orders and over owing waste containers. [5] This will support in holding the world hygienic. The waste collection is nished gradually capable, exciting, and functional [25]. ...
Internet drone, in modest things, is a kind of rapid automaton that is remotely organized by human beings. It is in machinery relations also known as Unmanned Floating Automobile (UFA). By numerous innovative developments in drone equipment, it has predictable that drones grow additional to contain analytical skills, specifically in the waste materials detection of images in smart cities. The drone offered in this article will be applied for the distribution of waste materials detection of images. Through a security system executed on the vessel involved, it confirms the protection of the waste materials detection of images till they get the correct place. The planned structure is an internet-based drone consuming the values of Internet of Things.
Still, the procedure of drones’ desires trained control and appropriate setup. Waste materials detection of images by internet drone are misguidedly measured as security drones, therefore has been criticized by prepared services.
Without the use of internet drones, operators are accountable for conveying things at a high cost. The goal of this internet drone is to monitor waste materials detection of images in smart cities. It is to detect waste material images and send them to administrator (Municipality). The internet drone is accomplished of finding these services and detected images statistics in a 360-degree cycle inside the smart cities. When broadcast is problematic, the self-maintaining internet drone are the ready for rapidly moving one location to another detect waste material images.
... Cybercriminals are always developing new tactics and software to get into systems and networks to gain, steal, and leak critical information, with cybercriminals constantly discovering new ways to penetrate our defenses. As a result, implementing an IDS is a critical and basic component of any decent security system [89,90]. Mixing several types of IDS and detection methods allows it to develop with the IT environment it monitors, as well as the threats and assaults lurking outside. ...
... The drone can be programmed in so many ways to perform various tasks, like a circle over a waypoint for a specific duration. This is the major factor contributing to aerial surveillance [32]. This method of programming the drone is called a preprogrammed mission. ...
Uninhabited Aerial System (UAS), or drones, are aircraft that could be remotely controlled and managed by a person or
have varying techniques, like autopilot support, and even fully autonomous modes that do not require human intervention.
In this paper, the drone can be remotely controlled and used for spying, among other things. The Pixhawk fight controller, combined with a transmitter and a receiver to transmit and receive radio signals for the drone’s remote control, makes
up the brains of the drone. The main components of this system are accompanied by four propellers for fight. The use of
an electronic speed controller (ESC) has been implemented to control and regulate the drone’s speed. Moreover, a lithium
polymer battery has been used to power up the drone. As previously indicated, the installation of the ESP Camera module
to this drone has been implemented, which will be utilized for live footage taken throughout its fight and to be able to relay
that footage to the user. The footage is analyzed for image processing and identifying objects in the video using the latest
You Only Look Once (YOLO) algorithm as surveillance is the primary function of this system.
... Protecting private or safety-critical infrastructures against attacks (e.g., espionage) by unmanned aerial vehicles (UAVs or drones) is an important security issue given the increasing prevalence of UAVs in commercial and private sectors (e.g., [1][2][3]). Therefore, there is also an increasing need for robust detection systems capable of reliably identifying and locating unauthorized UAVs. ...
The detection of drones or unmanned aerial vehicles is a crucial component in protecting safety-critical infrastructures and maintaining privacy for individuals and organizations. The widespread use of optical sensors for perimeter surveillance has made optical sensors a popular choice for data collection in the context of drone detection. However, efficiently processing the obtained sensor data poses a significant challenge. Even though deep learning-based object detection models have shown promising results, their effectiveness depends on large amounts of annotated training data, which is time consuming and resource intensive to acquire. Therefore, this work investigates the applicability of synthetically generated data obtained through physically realistic simulations based on three-dimensional environments for deep learning-based drone detection. Specifically, we introduce a novel three-dimensional simulation approach built on Unreal Engine and Microsoft AirSim for generating synthetic drone data. Furthermore, we quantify the respective simulation–reality gap and evaluate established techniques for mitigating this gap by systematically exploring different compositions of real and synthetic data. Additionally, we analyze the adaptation of the simulation setup as part of a feedback loop-based training strategy and highlight the benefits of a simulation-based training setup for image-based drone detection, compared to a training strategy relying exclusively on real-world data.
... W celu odpowiedzi na sformułowane problemy badawcze zastosowano teoretyczne metody badawcze obejmujące analizę i krytykę literatury. Pierwsza grupa literatury obejmowała publikacje naukowe odnoszące się do problematyki zastosowań dronów oraz zagrożeń wynikających z ich użytkowania w odniesieniu do bezpieczeństwa infrastruktury krytycznej, w tym portów lotniczych [4,9,10,19,24]. Druga grupa literatury to dokumenty normatywne w tym polityki instytucji i organizacji międzynarodowych odpowiedzialnych za bezpieczeństwo lotnictwa cywilnego w tym portów lotniczych [6,7,20,22]. W tym kontekście za kluczowe należy uznać opracowania Agencji Unii Europejskiej ds. ...
... Odrębną grupę metod kinetycznych stanowią te oparte na wykorzystaniu energii elektromagnetycznej o dużej mocy. Wykorzystanie impulsów elektromagnetycznych o dużej mocy lub precyzyjnej broni laserowej pozwala na fizyczne zniszczenie (zestrzelenie) drona poprzez zneutralizowanie jego obwodów elektronicznych [8,24]. Trzeba również pamiętać, że opcje zastrzelenia drona w obszarze portu lotniczego nie zawsze będą możliwe ze względu na ryzyko niekontrolowanego rozbicia się drona, szczególnie w obszarach kongestii. ...
Powszechny dostęp do bezzałogowych statków powietrznych popularnie określanych dronami powoduje, że coraz częściej zdarzają się incydenty z ich udziałem. Zagrożenia generowane przez drony w odniesieniu do infrastruktury krytycznej, w tym portów lotniczych, niezależnie od tego czy powstają w sposób intencjonalny czy nieintencjonalny stwarzają realne niebezpieczeństwo dla funkcjonowania portów lotniczych. Nieautoryzowane wtargnięcie drona w obszar portu lotniczego lub jego świadome wykorzystanie jako narzędzie aktu terrorystycznego mogą być bezpośrednią przyczyną kolizji ze statkiem powietrznym lub spowodować zniszczenie (uszkodzenie) infrastruktury portu lotniczego. Ochrona portu lotniczego przed tego rodzaju zagrożeniami wymaga kompleksowego podejścia obejmującego: wprowadzanie adekwatnych regulacji prawnych, zbudowanie systemu zapobiegania zagrożeniom, utrzymywanie w gotowości zasobów zapewniających wykrywanie, identyfikację i neutralizację zagrożeń generowanych przez drony oraz zbieranie doświadczeń.
... For fixed cameras, the latter strategy is preferred. Different image-based approaches are, for example, presented in [35,53,7,36,54,55,56]. The concept of a cascaded two-stage strategy is can also be applied to combine image-based methods with other modalities. ...
Following the success of deep learning-based models in various sequence processing tasks, these models are increasingly utilized in object tracking applications for motion prediction as a replacement of traditional approaches. On the one hand, these models can capture complex object dynamics while requiring less modeling, but on the other hand, they depend on a large amount of training data for parameter tuning.Towards this end, an approach for generating synthetic trajectory data of unmanned-aerial-vehicles (UAVs) in image space is presented in this paper. Since UAVs are dynamical systems, they are bound to strict physical constraints and inputs for controlling. Thus, they cannot move along arbitrary trajectories. To generate executable trajectories, it is possible to apply solutions from trajectory planning for our desired purpose of generating realistic UAV trajectory data. Accordingly, with the prerequisite that UAV trajectories fulfill a smoothness criterion corresponding to a minimal change of higher-order motion, planning methods enabling aggressive quadrotor flights are applied to generate optimal trajectories through a sequence of 3D waypoints. By projecting these maneuver trajectories to image space, a versatile trajectory data set is realized. To demonstrate the applicability of the synthetic trajectory data, we show that deep learning-based prediction models solely trained on the synthetically generated data can outperform traditional reference models on a real-world UAV tracking dataset. The evaluation is done on the publicly available ANTI-UAV dataset.KeywordsUnmanned-aerial-vehicle (UAV)Synthetic data generationTrajectory predictionDeep-learningSequence modelsTraining dataQuadrotors
