scholarly journals Detection and Tracking of UAV Targets Using Deep Learning

2021 ◽  
Author(s):  
Mohamed Khedir Noraldain Alamin
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Cross Section ◽  
Crucial Importance ◽  
Defense System ◽  
Tracking Method ◽  
Defense Systems ◽  
Detection And Tracking ◽  
Military Applications ◽  
Large Scope

In recent years, the use of Flying drones and modern Unmanned aerial vehicles (UAVs) with the latest techniques and capabilities for both civilian and military applications growing sustainably on a large scope, Drones could autonomously fly in several environments and locations and could perform various missions, providing a system for UAV detection and tracking represent crucial importance. This paper discusses Designing Detection and Tracking method as a part of Aero-vehicle Defense System (ADS) for UAVs using Deep learning algorithms. The small Radar cross-section (RCS) foot-print makes a problem for Traditional methods and Aero-vehicle Defense systems to distinguish between birds, stealth fighters, and UAVs incomparable of size and RCS characteristics, the detection is a challenge in low RCS targets because the chance of detection is incredibly less moreover, in the existence of interference and clutter which reduce the performance of detection process rapidly. 

scholarly journals Airborne Visual Detection and Tracking of Cooperative UAVs Exploiting Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4332 ◽  
Author(s):  
Roberto Opromolla ◽  
Giuseppe Inchingolo ◽  
Giancarmine Fasano
Keyword(s):  
Deep Learning ◽  
Visual Detection ◽  
Detection System ◽  
Flight Test ◽  
Target Range ◽  
Detection And Tracking ◽  
Military Applications ◽  
Technological Challenge ◽  
High Level ◽  
Processing Architecture

The performance achievable by using Unmanned Aerial Vehicles (UAVs) for a large variety of civil and military applications, as well as the extent of applicable mission scenarios, can significantly benefit from the exploitation of formations of vehicles able to fly in a coordinated manner (swarms). In this respect, visual cameras represent a key instrument to enable coordination by giving each UAV the capability to visually monitor the other members of the formation. Hence, a related technological challenge is the development of robust solutions to detect and track cooperative targets through a sequence of frames. In this framework, this paper proposes an innovative approach to carry out this task based on deep learning. Specifically, the You Only Look Once (YOLO) object detection system is integrated within an original processing architecture in which the machine-vision algorithms are aided by navigation hints available thanks to the cooperative nature of the formation. An experimental flight test campaign, involving formations of two multirotor UAVs, is conducted to collect a database of images suitable to assess the performance of the proposed approach. Results demonstrate high-level accuracy, and robustness against challenging conditions in terms of illumination, background and target-range variability.

A unified framework for automated person re-indentification

2020 ◽  
Vol 71 (7) ◽  
pp. 868-880
Author(s):  
Nguyen Hong-Quan ◽  
Nguyen Thuy-Binh ◽  
Tran Duc-Long ◽  
Le Thi-Lan
Keyword(s):  
Deep Learning ◽  
Video Analysis ◽  
Camera Network ◽  
Unified Framework ◽  
Person Detection ◽  
Practical Applications ◽  
Detection And Tracking ◽  
Analysis System ◽  
Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

A Deep Learning Model for Detection and Tracking in High-Throughput Images of Organoid

2021 ◽  
pp. 104490
Author(s):  
Xuesheng Bian ◽  
Gang Li ◽  
Cheng Wang ◽  
Weiquan Liu ◽  
Xiuhong Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Learning Model ◽  
Detection And Tracking ◽  
Deep Learning Model

scholarly journals A new nonlinear lifting line method for aerodynamic analysis and deep learning modeling of small unmanned aerial vehicles

2021 ◽  
Vol 13 ◽  
pp. 175682932110168
Author(s):  
Hasan Karali ◽  
Gokhan Inalhan ◽  
M Umut Demirezen ◽  
M Adil Yukselen
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Computationally Intensive ◽  
Lifting Line ◽  
Lifting Line Method ◽  
Nonlinear Aerodynamic

In this work, a computationally efficient and high-precision nonlinear aerodynamic configuration analysis method is presented for both design optimization and mathematical modeling of small unmanned aerial vehicles. First, we have developed a novel nonlinear lifting line method which (a) provides very good match for the pre- and post-stall aerodynamic behavior in comparison to experiments and computationally intensive tools, (b) generates these results in order of magnitudes less time in comparison to computationally intensive methods such as computational fluid dynamics. This method is further extended to a complete configuration analysis tool that incorporates the effects of basic fuselage geometries. Moreover, a deep learning based surrogate model is developed using data generated by the new aerodynamic tool that can characterize the nonlinear aerodynamic performance of unmanned aerial vehicles. The major novel feature of this model is that it can predict the aerodynamic properties of unmanned aerial vehicle configurations by using only geometric parameters without the need for any special input data or pre-process phase as needed by other computational aerodynamic analysis tools. The obtained black-box function can calculate the performance of an unmanned aerial vehicle over a wide angle of attack range on the order of milliseconds, whereas computational fluid dynamics solutions take several days/weeks in a similar computational environment. The aerodynamic model predictions show an almost 1-1 coincidence with the numerical data even for configurations with different airfoils that are not used in model training. The developed model provides a highly capable aerodynamic solver for design optimization studies as demonstrated through an illustrative profile design example.

Crop stem detection and tracking for precision hoeing using deep learning

2022 ◽  
Vol 192 ◽  
pp. 106606
Author(s):  
Louis Lac ◽  
Jean-Pierre Da Costa ◽  
Marc Donias ◽  
Barna Keresztes ◽  
Alain Bardet
Keyword(s):  
Deep Learning ◽  
Detection And Tracking
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