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A Vehicle Detection Method for Aerial Image Based on YOLO

Junyan Lu, Chi Ma, Li Li, Xiaoyan Xing, Yong Zhang, Zhigang Wang, Jiuwei Xu
2018 Journal of Computer and Communications  
In this paper, a vehicle detection method for aerial image based on YOLO deep learning algorithm is presented.  ...  With the application of UAVs in intelligent transportation systems, vehicle detection for aerial images has become a key engineering technology and has academic research significance.  ...  YOLO Deep Learning Object Detection Algorithm YOLO, which has been proposed by Joseph Redmon and others in 2015 [6] , is a real-time object detection system based on CNN (Convolutional Neural Network)  ... 
doi:10.4236/jcc.2018.611009 fatcat:lvatfcybpraxzk4gqs3r7vng3e

2021 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 14

2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS 2021 730-746 Pan-Sharpening Based on Convolutional Neural Network by Using the Loss Function With No-Reference.  ...  ., +, JSTARS 2021 249-257 Pan-Sharpening Based on Convolutional Neural Network by Using the Loss Function With No-Reference.  ...  ., Hyperspectral Image Superresolution via Deep Structure and Texture Interfusion; JSTARS 2021 8665-8678 Hu, J., see Feng, D., JSTARS 2021 12212-12223 Hu, J., Shen, X., Yu, H., Shang, X., Guo, Q.,  ... 
doi:10.1109/jstars.2022.3143012 fatcat:dnetkulbyvdyne7zxlblmek2qy

Vehicle Detection in Aerial Images Using Rotation-Invariant Cascaded Forest

Bodi Ma, Zhenbao Liu, Feihong Jiang, Yuehao Yan, Jinbiao Yuan, Shuhui Bu
2019 IEEE Access  
To solve this problem, this paper introduces a novel rotationinvariant vehicle detection method which is accurate, stable and has a simple structure compared with region-based convolutional network method  ...  INDEX TERMS Rotation invariant, vehicle detection, cascaded forest, aerial images.  ...  In the late years, region-based convolutional neural network (CNN) [11] is widely utilized in the object detection methods.  ... 
doi:10.1109/access.2019.2915368 fatcat:nak3maahmnei7k7kxgv4tzhjuq

Parallel CNN Network Learning-Based Video Object Recognition for UAV Ground Detection

Huanyu Liu, Jiaqing Qiao, Lu Li, Lei Wang, Hongyu Chu, Qingyu Wang, M. Hassaballah
2022 Wireless Communications and Mobile Computing  
transformation of the training data, the neural network can learn the rotation invariance of the target.  ...  Video object recognition for UAV ground detection is widely used in target search, daily patrol, environmental reconnaissance, and other fields.  ...  The target detection algorithm based on deep learning has fast detection speed and high detection accuracy.  ... 
doi:10.1155/2022/2701217 fatcat:xbsbknzjizfcrnoxhzvvaab7xu

Sensor-Based Environmental Perception Technology for Intelligent Vehicles

Biyao Wang, Yi Han, Di Tian, Tian Guan, Haibin Lv
2021 Journal of Sensors  
Finally, the paper looks forward to the research direction of sense-based intelligent vehicle perception technology, which will play an important role in guiding the development of intelligent vehicles  ...  The functions of the intelligent vehicle assistance system which has been applied to the ground at present are described, and the lane detection, adaptive cruise control (ACC), and autonomous emergency  ...  Numerous learning networks, such as Recurrent Neural Network (RNN) [3] , Deep Boltzmann Machine (DBN) [4] , Generative Adversarial Networks (GAN), Long Short-Term Memory (LSTM), Region-Based Convolutional  ... 
doi:10.1155/2021/8199361 fatcat:hw4m3ikkhfcstl33nxmdcdcxzu

Target Detection of Low-Altitude UAV Based on Improved YOLOv3 Network

Haiqing Zhai, Yang Zhang, Shan Zhong
2022 Journal of Robotics  
A low-altitude unmanned aerial vehicle (UAV) target detection method based on an improved YOLOv3 network is proposed.  ...  Most existing methods are difficult to detect low-altitude and fast-moving drones.  ...  Liu and Zhang [15] proposed an automatic vehicle detection method based on deep learning.  ... 
doi:10.1155/2022/4065734 fatcat:hwoie5pxe5fobpxt5vimlos35e

Detection And Recognition Of Objects In Digital Images Using Elm Classification

R. Bhuvaneswari, Dr. Ravi Subban
2018 Zenodo  
Taking this into account, this article presents an effective time conserving object recognition approach based on three important phases.  ...  Initially, the points of interest are selected by means of Generalized Kadir Brady (GKB) detector, which considers the geometry and texture pattern of the images.  ...  An object detection system based on depth information of images is presented in [5] . The depth information of the images is used in combination with RGB upon the Convolutional Neural Network (CNN).  ... 
doi:10.5281/zenodo.1230623 fatcat:3ddubvysjrefnewlzx5gncnf3y

2020 Index IEEE Transactions on Industrial Informatics Vol. 16

2020 IEEE Transactions on Industrial Informatics  
see Daneshvar, M., TII June 2020 3928-3941 Mohtat, P., see Lee, S., TII May 2020 3376-3386 Momeni, H., Sadoogi, N., Farrokhifar, M., and Gharibeh, H.F., Fault Diagnosis in Photovoltaic Arrays Using  ...  on Intelligent Clustering in Local Area Industrial IoT Systems; TII June 2020 3697-3707 Jia, S., see Chen, C., 1873-1884 Jia, W., see Wang, T., 2054-2062 Jia, W., see Lian, J., 1343-1351 Jia, W., see  ...  ., +, TII Jan. 2020 373-383 Traffic Network Flow Prediction Using Parallel Training for Deep Convolutional Neural Networks on Spark Cloud.  ... 
doi:10.1109/tii.2021.3053362 fatcat:blfvdtsc3fdstnk6qoaazskd3i

Crypto Makes AI Evolve [article]

Behrouz Zolfaghari
2022 arXiv   pre-print
Then, we establish a future roadmap for further research in this area, focusing on the role of quantum-inspired and bio-inspired AI.  ...  They first performed experiments to determine regions of interest. Then a convolutional neural network is used to train a set of bio-inspired features.  ...  Li et al. use quantum-inspired reinforcement learning to plan the trajectory of unmanned aerial vehicles [139] .  ... 
arXiv:2206.12669v1 fatcat:gm7hoplpnngrnc3ty53yfyfcrq

2021 Index IEEE Transactions on Instrumentation and Measurement Vol. 70

2021 IEEE Transactions on Instrumentation and Measurement  
Article numbers are based on specified topic areas and corresponding codes associated with the publication.  ...  Yang, J., +, TIM 2021 3525712 Conductive Particle Detection for Chip on Glass Using Convolutional Neural Network.  ...  Yang, J., +, TIM 2021 3525712 Conductive Particle Detection for Chip on Glass Using Convolutional Neural Network.  ... 
doi:10.1109/tim.2022.3156705 fatcat:dmqderzenrcopoyipv3v4vh4ry

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing [article]

Ping Lang, Xiongjun Fu, Marco Martorella, Jian Dong, Rui Qin, Xianpeng Meng, Min Xie
2020 arXiv   pre-print
The main applications of ML-based RSP are then analysed and structured based on the application field.  ...  Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments.  ...  The former firstly generates positive region proposals to discard the most of negative samples, then performs the candidate regions classification, such as region convolutional neural networks (R-CNN)  ... 
arXiv:2009.13702v1 fatcat:m6am73324zdwba736sn3vmph3i

EYNet: Extended YOLO for Airport Detection in Remote Sensing Images [article]

Hengameh Mirhajianmoghadam, Behrouz Bolourian Haghighi
2022 arXiv   pre-print
In particular, uncrewed and operated aerial vehicles must immediately detect safe areas to land in emergencies.  ...  In this way, MobileNet and ResNet18, with fewer layers and parameters retrained on a similar dataset, are parallelly trained as base networks.  ...  The usage of Deep Neural Network (DNN) as an object detecting method commenced in 2014 by proposing a Regionbased Convolutional Neural Networks(R-CNN) [11] .  ... 
arXiv:2203.14007v1 fatcat:frlzcwoocneftcixvrhul4cu6q

Oriented Ship Detector for Remote Sensing Imagery Based on Pairwise Branch Detection Head and SAR Feature Enhancement

Bokun He, Qingyi Zhang, Ming Tong, Chu He
2022 Remote Sensing  
Recently, object detection in natural images has made a breakthrough, but it is still challenging in oriented ship detection for remote sensing imagery.  ...  on the pairwise branch detection head and adaptive SAR feature enhancement.  ...  [35] used the full convolution neural network to detect the presence of remotely sensed ships and then obtained the accurate position of ship targets through the visual attention mechanism.  ... 
doi:10.3390/rs14092177 fatcat:eyzlemgztrhmddqcvtyuvetla4

MRENet: Simultaneous Extraction of Road Surface and Road Centerline in Complex Urban Scenes from Very High-Resolution Images

Zhenfeng Shao, Zifan Zhou, Xiao Huang, Ya Zhang
2021 Remote Sensing  
Most existing road datasets are based on data with simple and clear backgrounds under ideal conditions, such as images derived from Google Earth.  ...  We then propose a two-task and end-to-end convolution neural network, termed Multitask Road-related Extraction Network (MRENet), for road surface extraction and road centerline extraction.  ...  Acknowledgments: We would like to thank the anonymous reviewers for their constructive and valuable suggestions on the earlier drafts of this manuscript.  ... 
doi:10.3390/rs13020239 fatcat:mwfh76rqhfgsppn5udml3dwomq

UAV-Based Remote Sensing Applications for Bridge Condition Assessment

Sainab Feroz, Saleh Abu Dabous
2021 Remote Sensing  
Non-destructive testing (NDT) using Unmanned Aerial Vehicles (UAVs) have been gaining momentum for bridge monitoring in the recent years, particularly due to enhanced accessibility and cost efficiency,  ...  This study compiled sixty-five journal and conference papers published in the last two decades scrutinizing NDT-based UAV systems.  ...  Kim et al. used deep learning convolutional neural networks for detection and measurement of crack geometry via image classification and localization [79] .  ... 
doi:10.3390/rs13091809 fatcat:czb4tvnfmbfttf3mvwl6jclgxq
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