The Development of a UAV Target Tracking System Based on YOLOv3-Tiny Object Detection Algorithm

Gan, Lu; Tan, Ying; Chen, Lingfeng; Kuang, Wenchuan; Li, Binghua; Duan, Feng; Zhu, Chi

doi:10.1109/robio54168.2021.9739612

Cited by 4 publications

(2 citation statements)

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“…YOLO (You-Only-Look-Once) is a popular object detection algorithm that has been widely used in computer vision applications [25,26]. Compared to other object detection algorithms, YOLO takes a different approach by dividing the input image into a grid and predicting the probability of objects in each cell of the grid, along with their bounding boxes and class labels.…”

Section: Deep Learning For Ssamentioning

confidence: 99%

Deep learning‐based space debris detection for space situational awareness: A feasibility study applied to the radar processing

Massimi,

Ferrara,

Petrucci

et al. 2024

IET Radar Sonar & Navi

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The increasing number of space objects (SO), debris, and constellation of satellites in Low Earth Orbit poses a significant threat to the sustainability and safety of space operations, which must be carefully and efficiently addressed to avoid mutual collisions. The space situational awareness is currently addressed by an ensemble of radar and radio‐telescopes that detect and track SO. However, a large part of space debris is composed of very small and tiny metallic objects, very difficult to detect. The authors demonstrate the benefits of using deep learning (DL) architectures for small space object detection by radar observations. TIRA radio telescope has been simulated to generate range‐Doppler maps, then used as inputs for object detection exploiting You‐Only‐Look‐Once (YOLO) frameworks. The results demonstrate that the object detection by using YOLO algorithms outperform conventional target detection approaches, thus indicating the potential benefits of using DL techniques for space surveillance applications.

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Section: Deep Learning For Ssamentioning

confidence: 99%

Deep learning‐based space debris detection for space situational awareness: A feasibility study applied to the radar processing

Massimi,

Ferrara,

Petrucci

et al. 2024

IET Radar Sonar & Navi

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“…Then, YOLOv3-Tiny analyzes the images collected by the UAV for target tracking. Finally, the tracking system control signal is sent to the flight controller of the UAV [ 105 ].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

Massimi

Ferrara²,

Benedetto

2022

Sensors

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Artificial Intelligence of things (AIoT) is the combination of Artificial Intelligence (AI) technologies and the Internet of Things (IoT) infrastructure. AI deals with the devices’ learning process to acquire knowledge from data and experience, while IoT concerns devices interacting with each other using the Internet. AIoT has been proven to be a very effective paradigm for several existing applications as well as for new areas, especially in the field of satellite communication systems with mega-constellations. When AIoT meets space communications efficiently, we have interesting uses of AI for Satellite IoT (SIoT). In fact, the number of space debris is continuously increasing as well as the risk of space collisions, and this poses a significant threat to the sustainability and safety of space operations that must be carefully and efficiently addressed to avoid critical damage to the SIoT networks. This paper aims to provide a systematic survey of the state of the art, challenges, and perspectives on the use of deep learning methods for space situational awareness (SSA) object detection and classification. The contributions of this paper can be summarized as follows: (i) we outline using AI algorithms, and in particular, deep learning (DL) methods, the possibility of identifying the nature/type of spatial objects by processing signals from radars; (ii) we present a comprehensive taxonomy of DL-based methods applied to SSA object detection and classification, as well as their characteristics, and implementation issues.

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A survey on cybersecurity attacks and defenses for unmanned aerial systems

Wang

et al. 2023

Journal of Systems Architecture

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The Development of a UAV Target Tracking System Based on YOLOv3-Tiny Object Detection Algorithm

Cited by 4 publications

References 0 publications

Deep learning‐based space debris detection for space situational awareness: A feasibility study applied to the radar processing

Deep learning‐based space debris detection for space situational awareness: A feasibility study applied to the radar processing

Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

A survey on cybersecurity attacks and defenses for unmanned aerial systems

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