Autonomous drone hunter operating by deep learning and all-onboard computations in GPS-denied environments

Wyder, Philippe M.; Chen, Yansong; Lasrado, Adrian J.; Pelles, Rafael J.; Kwiatkowski, Robert; Comas, Edith O. A.; Kennedy, Richard; Mangla, Arjun; Huang, Zixi; Hu, Xiaotian; Xiong, Zhiyao; Aharoni, Tomer; Chuang, Tzu-Chan; Lipson, Hod

doi:10.1371/journal.pone.0225092

Cited by 35 publications

(23 citation statements)

References 18 publications

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“… Aerial systems such as defense UAVs and birds of prey can be used to crash into the hostile UAV or destroy it by using offensive mechanisms such as claws and remote weaponry. [137] . High Power Microwave (HPM) devices disrupt electron- ics by generating an Electromagnetic Pulse (EMP).…”

Section: Uav Neutralizing Techniquesmentioning

confidence: 99%

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A Comprehensive Review of Unmanned Aerial Vehicle Attacks and Neutralization Techniques

et al. 2021

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Unmanned Aerial Vehicles (UAV) have revolution- ized the aircraft industry in this decade. UAVs are now capable of carrying out remote sensing, remote monitoring, courier delivery, and a lot more. A lot of research is happening on making UAVs more robust using energy harvesting techniques to have a better battery lifetime, network performance and to secure against attackers. UAV networks are many times used for unmanned missions. There have been many attacks on civilian, military, and industrial targets that were carried out using remotely controlled or automated UAVs. This continued misuse has led to research in preventing unauthorized UAVs from causing damage to life and property. In this paper, we present a literature review of UAVs, UAV attacks, and their prevention using anti-UAV techniques. We first discuss the different types of UAVs, the regulatory laws for UAV activities, their use cases, recreational, and military UAV incidents. After understanding their operation, various techniques for monitoring and preventing UAV attacks are described along with case studies.

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Section: Uav Neutralizing Techniquesmentioning

confidence: 99%

“…Aerial systems such as defense UAVs and birds of prey can be used to crash into the hostile UAV or destroy it by using offensive mechanisms such as claws and remote weaponry. [137] .…”

Section: Uav Neutralizing Techniquesmentioning

confidence: 99%

A Comprehensive Review of Unmanned Aerial Vehicle Attacks and Neutralization Techniques

et al. 2021

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“…Wyder et al (25) presented a UAV platform to detect and counter a small UAV in an indoor environment where Global Positioning System (GPS) is not available. An image dataset is used to train a Tiny Yolo object detection algorithm.…”

Section: Drone Detection Modelsmentioning

confidence: 99%

Improving real-time drone detection for counter-drone systems

2021

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The number of unmanned aerial vehicles (UAVs, also known as drones) has increased dramatically in the airspace worldwide for tasks such as surveillance, reconnaissance, shipping and delivery. However, a small number of them, acting maliciously, can raise many security risks. Recent Artificial Intelligence (AI) capabilities for object detection can be very useful for the identification and classification of drones flying in the airspace and, in particular, are a good solution against malicious drones. A number of counter-drone solutions are being developed, but the cost of drone detection ground systems can also be very high, depending on the number of sensors deployed and powerful fusion algorithms. We propose a low-cost counter-drone solution composed uniquely by a guard-drone that should be able to detect, locate and eliminate any malicious drone. In this paper, a state-of-the-art object detection algorithm is used to train the system to detect drones. Three existing object detection models are improved by transfer learning and tested for real-time drone detection. Training is done with a new dataset of drone images, constructed automatically from a very realistic flight simulator. While flying, the guard-drone captures random images of the area, while at the same time, a malicious drone is flying too. The drone images are auto-labelled using the location and attitude information available in the simulator for both drones. The world coordinates for the malicious drone position must then be projected into image pixel coordinates. The training and test results show a minimum accuracy improvement of 22% with respect to state-of-the-art object detection models, representing promising results that enable a step towards the construction of a fully autonomous counter-drone system.

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“…Sometimes existing approaches work just fine. For example, Wyder et al [2019], tracked moving objects in real-time using drones with a neural networkbased object detector (You Only Look Once, or YOLO Redmon et al [2016]). While this algorithm does not have the best detection accuracy among similar, more computationally intensive algorithms (e.g., deeper neural networks, or architectures that explicitly model sequences of images), YOLO is computationally efficient, allowing for high frame rate object detection with limited computing power.…”

Section: Know When To Innovatementioning

confidence: 99%

Ten simple rules for working with high resolution remote sensing data

Joseph¹,

Spiers

Koontz³

et al. 2021

Preprint

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Researchers in Earth and environmental science can extract incredible value from high resolution remote sensing data, but these data can be hard to use. Pain free use requires skills from remote sensing and the data sciences that are seldom taught together. In practice, many researchers teach themselves how to use high resolution remote sensing data with ad hoc trial and error processes, often resulting in wasted effort and resources. Here we outline ten “rules” with examples from Earth and environmental science to help applied researchers work more effectively with high resolution data.

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Autonomous drone hunter operating by deep learning and all-onboard computations in GPS-denied environments

Cited by 35 publications

References 18 publications

A Comprehensive Review of Unmanned Aerial Vehicle Attacks and Neutralization Techniques

A Comprehensive Review of Unmanned Aerial Vehicle Attacks and Neutralization Techniques

Improving real-time drone detection for counter-drone systems

Ten simple rules for working with high resolution remote sensing data

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