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Real-Time Ground Vehicle Detection in Aerial Infrared Imagery Based on Convolutional Neural Network

Xiaofei Liu, Tao Yang, Jing Li
2018 Electronics  
In this paper, a novel approach towards ground vehicle detection in aerial infrared images based on a convolutional neural network is proposed.  ...  An infrared sensor is a commonly used imaging device. Unmanned aerial vehicles, the most promising moving platform, each play a vital role in their own field, respectively.  ...  Conclusions This paper proposes an efficient method for real-time ground vehicle detection in infrared imagery based on a convolutional neural network.  ... 
doi:10.3390/electronics7060078 fatcat:vqb4lm2w7nh6pjvnqyutd24xv4

A SMALL TARGET VISUAL TRACKING METHOD FOR UNMANNED AERIAL VEHICLE PLATFORM UNDER CONVOLUTIONAL NEURAL NETWORK

2020 International Journal of Mechatronics and Applied Mechanics  
Firstly, in order to solve the real-time requirement of small target detection of UAV platform, the convolutional neural network target detection model is improved.  ...  In order to explore the easy loss of tracking target in the realization of Unmanned Aerial Vehicle (UAV) platform visual target tracking, the convolutional neural network algorithm is proposed to improve  ...  Figure 1 : 1 Target detection model of Mobile Net V2 based on convolutional neural network A Small Target Visual Tracking Method for Unmanned Aerial Vehicle Platform under Convolutional Neural Network  ... 
doi:10.17683/ijomam/issue8.7 fatcat:6d73roge6rcwpk2dls5jieexda

DroNet: Efficient convolutional neural network detector for real-time UAV applications

Christos Kyrkou, George Plastiras, Theocharis Theocharides, Stylianos I. Venieris, Christos-Savvas Bouganis
2018 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE)  
This paper therefore, explores the trade-offs involved in the development of a single-shot object detector based on deep convolutional neural networks (CNNs) that can enable UAVs to perform vehicle detection  ...  Through the analysis we propose a CNN architecture that is capable of detecting vehicles from aerial UAV images and can operate between 5-18 frames-per-second for a variety of platforms with an overall  ...  ACKNOWLEDGMENT Christos Kyrkou gratefully acknowledges the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.  ... 
doi:10.23919/date.2018.8342149 dblp:conf/date/KyrkouPTVB18 fatcat:73nr4wvsnvcehjzgphiil3n5ey

Autonomous trail‐following unmanned aerial vehicle system based on resource partitioning of single hardware platform

Yoojin Lim, Kyungil Kim, Jinah Shin, Chaedeok Lim
2021 Electronics Letters  
As deep neural networks are spreading to almost all fields, flight systems in the unmanned aerial vehicle (UAV) domain are undergoing various transitions to intelligent systems.  ...  Introduction: Recently, deep neural network (DNN) technologies have been actively studied and applied in order to raise the bar regarding intelligence in system control domains, such as unmanned aerial  ...  As deep neural networks are spreading to almost all fields, flight systems in the unmanned aerial vehicle (UAV) domain are undergoing various transitions to intelligent systems.  ... 
doi:10.1049/ell2.12099 fatcat:dzd4d3u3s5h5bgdb32npe5bfwi

Vision-assisted Landing Method for Unmanned Powered Parachute Vehicle Based on Lightweight Neural Network

Mengxuan Zhang, Wei Hu, Shude Ji, Qi Song, Peng Gong, Lingpei Kong
2021 IEEE Access  
INTRODUCTION Unmanned powered parachute vehicles (UPPVs) are a new type of unmanned aerial vehicle based on manned powered parachutes.  ...  Its backbone network CSPDarknet53 has higher accuracy and real-time performance in target detection.  ...  Author Name: Preparation of Papers for IEEE Access (February 2017) VOLUME XX, 2017 10  ... 
doi:10.1109/access.2021.3112185 fatcat:jd2hn3og2zhh7kclmcaqyjd5tu

Current trends in the development of intelligent unmanned autonomous systems

Tao Zhang, Qing Li, Chang-shui Zhang, Hua-wei Liang, Ping Li, Tian-miao Wang, Shuo Li, Yun-long Zhu, Cheng Wu
2017 Frontiers of Information Technology & Electronic Engineering  
Furthermore, we classify the relevant technologies into seven areas, including AI technologies, unmanned vehicles, unmanned aerial vehicles, service robots, space robots, marine robots, and unmanned workshops  ...  This paper introduces the trends in the development of intelligent unmanned autonomous systems by summarizing the main achievements in each technological platform.  ...  Trends in unmanned aerial vehicle development Overview of unmanned aerial vehicles An unmanned aerial vehicle (UAV), commonly known as a drone, is an unmanned aircraft system (Wikipedia, 2016a) .  ... 
doi:10.1631/fitee.1601650 fatcat:rrhhym5waff7renjujhq7ovvdm

Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles [article]

Christos Kyrkou, Theocharis Theocharides
2019 arXiv   pre-print
Through this analysis a lightweight convolutional neural network (CNN) architecture is developed, capable of running efficiently on an embedded platform achieving ~3x higher performance compared to existing  ...  These preliminary results provide a solid basis for further experimentation towards real-time aerial image classification for emergency response applications using UAVs.  ...  Acknowledgements Christos Kyrkou would like to acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.  ... 
arXiv:1906.08716v1 fatcat:i5rorkvpjnd6vodxrnjuhv5cma

Fast and Accurate, Convolutional Neural Network Based Approach for Object Detection from UAV [article]

Xiaoliang Wang, Peng Cheng, Xinchuan Liu, Benedict Uzochukwu
2019 arXiv   pre-print
Unmanned Aerial Vehicles (UAVs), have intrigued different people from all walks of life, because of their pervasive computing capabilities.  ...  State-of-the-art performance has been achieved in utilizing focal loss dense detector RetinaNet based approach for object detection from UAV in a fast and accurate manner.  ...  For the next step of the study, we plan to investigate further the use of different implementation platforms to deploy Convolutional Neural Network based methods within UAV system.  ... 
arXiv:1808.05756v2 fatcat:ojtu5ymmujduhabxwlpip3azgm

Smart Sensors and Devices in Artificial Intelligence

Dan Zhang, Bin Wei
2020 Sensors  
As stated in the Special Issue call, "sensors are eyes or/and ears of an intelligent system, such as Unmanned Aerial Vehicle (UAV), Automated Guided Vehicle (AGV) and robots [...]  ...  The papers in this Special Issue illustrate the breadth and depth of sensor technologies applied for solving different problems.  ...  Thanks are also given to all the hard working reviewers for their detailed comments and suggestions.  ... 
doi:10.3390/s20205945 pmid:33096695 fatcat:eelnvjmjfbg7thdgzzbuniwxci

SSSDET: Simple Short and Shallow Network for Resource Efficient Vehicle Detection in Aerial Scenes

Murari Mandal, Manal Shah, Prashant Meena, Santosh Kumar Vipparthi
2019 2019 IEEE International Conference on Image Processing (ICIP)  
In this paper we propose a simple short and shallow network (SSSDet) to robustly detect and classify small-sized vehicles in aerial scenes.  ...  The commonly deployed low cost unmanned aerial vehicles (UAVs) for aerial scene analysis are highly resource constrained in nature.  ...  The authors would like to thank the members of Vision Intelligence Lab and Kautilya Bhardwaj for their valuable support. We are also thankful to NVIDIA for providing TITAN Xp GPU grant.  ... 
doi:10.1109/icip.2019.8803262 dblp:conf/icip/MandalSMV19 fatcat:k33fmj3vrbgovctwrlnxra6tgi

Front Matter: Volume 11758

Paul L. Muench, Hoa G. Nguyen, Brian K. Skibba
2021 Unmanned Systems Technology XXIII  
SPIEDigitalLibrary.org Paper Numbering: A unique citation identifier (CID) number is assigned to each article in the Proceedings of SPIE at the time of publication.  ...  Publication of record for individual papers is online in the SPIE Digital Library.  ...  SPIEDigitalLibrary.org Paper Numbering: A unique citation identifier (CID) number is assigned to each article in the Proceedings of SPIE at the time of publication.  ... 
doi:10.1117/12.2598687 fatcat:fnuurnuuabez5ppp7fdfnz2nwq

Object Recognition in Aerial Images Using Convolutional Neural Networks

Matija Radovic, Offei Adarkwa, Qiaosong Wang
2017 Journal of Imaging  
This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition.  ...  Furthermore, using a convolutional neural network implemented in the "YOLO" ("You Only Look Once") platform, objects can be tracked, detected ("seen"), and classified ("comprehended") from video feeds  ...  Introduction There are a wide range of applications for unmanned aerial vehicles (UAVs) in the civil engineering field.  ... 
doi:10.3390/jimaging3020021 fatcat:guyeiwpfbrghjhlzwubxbkhdcy

ShuffleDet: Real-Time Vehicle Detection Network in On-Board Embedded UAV Imagery [chapter]

Seyed Majid Azimi
2019 Landolt-Börnstein - Group III Condensed Matter  
On-board real-time vehicle detection is of great significance for UAVs and other embedded mobile platforms.  ...  We propose a computationally inexpensive detection network for vehicle detection in UAV imagery which we call ShuffleDet.  ...  Introduction On-board real-time processing of data through embedded systems plays a crucial role in applying the images acquired from the portable platforms (e.g., unmanned aerial vehicless (UAVs)) to  ... 
doi:10.1007/978-3-030-11012-3_7 fatcat:q4oiyawaifgg3j75drlg2hmjlu

ShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV Imagery [article]

Seyed Majid Azimi
2018 arXiv   pre-print
On-board real-time vehicle detection is of great significance for UAVs and other embedded mobile platforms.  ...  We propose a computationally inexpensive detection network for vehicle detection in UAV imagery which we call ShuffleDet.  ...  Introduction On-board real-time processing of data through embedded systems plays a crucial role in applying the images acquired from the portable platforms (e.g., unmanned aerial vehicless (UAVs)) to  ... 
arXiv:1811.06318v1 fatcat:svyd5okvrzcz7nncjog7qlxety

Vehicle Detection in Aerial Images Based on 3D Depth Maps and Deep Neural Networks

Saleh Javadi, Mattias Dahl, Mats I. Pettersson
2021 IEEE Access  
INDEX TERMS Convolutional neural networks, 3D depth maps, object detection, aerial images.  ...  In this article, we investigate the ability of three-dimensional (3D) feature maps to improve the performance of deep neural network (DNN) for vehicle detection.  ...  Rameez from BTH for the valuable discussions.  ... 
doi:10.1109/access.2021.3049741 fatcat:wy7sqskjpbarza6ty2ysahvipe
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