Marker images detection algorithm for the unmanned aerial vehicle vertical landing

Trudy MAI ◽  
2021 ◽  
pp. 13-13
Author(s):  
Mikhail Trusfus ◽  
Ilfir Abdullin
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Detection Algorithm ◽  
Aerial Vehicle ◽  
Vertical Landing

scholarly journals Motion Detection Algorithm for Unmanned Aerial Vehicle Nighttime Surveillance

2014 ◽  
Vol E97.D (12) ◽  
pp. 3248-3251
Author(s):  
Huaxin XIAO ◽  
Yu LIU ◽  
Wei WANG ◽  
Maojun ZHANG
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Motion Detection ◽  
Detection Algorithm ◽  
Aerial Vehicle

scholarly journals Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection

2021 ◽  
Vol 13 (21) ◽  
pp. 4377
Author(s):  
Long Sun ◽  
Jie Chen ◽  
Dazheng Feng ◽  
Mengdao Xing
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Target Detection ◽  
Template Matching ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Information Warfare ◽  
Aerial Vehicle ◽  
Uav Images

Unmanned aerial vehicle (UAV) is one of the main means of information warfare, such as in battlefield cruises, reconnaissance, and military strikes. Rapid detection and accurate recognition of key targets in UAV images are the basis of subsequent military tasks. The UAV image has characteristics of high resolution and small target size, and in practical application, the detection speed is often required to be fast. Existing algorithms are not able to achieve an effective trade-off between detection accuracy and speed. Therefore, this paper proposes a parallel ensemble deep learning framework for unmanned aerial vehicle video multi-target detection, which is a global and local joint detection strategy. It combines a deep learning target detection algorithm with template matching to make full use of image information. It also integrates multi-process and multi-threading mechanisms to speed up processing. Experiments show that the system has high detection accuracy for targets with focal lengths varying from one to ten times. At the same time, the real-time and stable display of detection results is realized by aiming at the moving UAV video image.

Numerical Simulation of the Vertical Landing of Unmanned Aerial Vehicle with Ducted Fan Propulsors by Eddy-Resolving Methods

2020 ◽  
Vol 63 (4) ◽  
pp. 776-780
Author(s):  
D. V. Rybakov ◽  
P. S. Chernyshov ◽  
L. O. Vokin ◽  
N. V. Prodan
Keyword(s):  
Numerical Simulation ◽  
Unmanned Aerial Vehicle ◽  
Ducted Fan ◽  
Aerial Vehicle ◽  
Vertical Landing

scholarly journals Autonomous, Onboard Vision-Based Trash and Litter Detection in Low Altitude Aerial Images Collected by an Unmanned Aerial Vehicle

2021 ◽  
Vol 13 (5) ◽  
pp. 965
Author(s):  
Marek Kraft ◽  
Mateusz Piechocki ◽  
Bartosz Ptak ◽  
Krzysztof Walas
Keyword(s):  
Neural Networks ◽  
Unmanned Aerial Vehicle ◽  
Low Cost ◽  
Detection Algorithm ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Deep Convolutional Neural Networks ◽  
Domain Specific ◽  
Aerial Vehicle ◽  
Low Altitude

Public littering and discarded trash are, despite the effort being put to limit it, still a serious ecological, aesthetic, and social problem. The problematic waste is usually localised and picked up by designated personnel, which is a tiresome, time-consuming task. This paper proposes a low-cost solution enabling the localisation of trash and litter objects in low altitude imagery collected by an unmanned aerial vehicle (UAV) during an autonomous patrol mission. The objects of interest are detected in the acquired images and put on the global map using a set of onboard sensors commonly found in typical UAV autopilots. The core object detection algorithm is based on deep, convolutional neural networks. Since the task is domain-specific, a dedicated dataset of images containing objects of interest was collected and annotated. The dataset is made publicly available, and its description is contained in the paper. The dataset was used to test a range of embedded devices enabling the deployment of deep neural networks for inference onboard the UAV. The results of measurements in terms of detection accuracy and processing speed are enclosed, and recommendations for the neural network model and hardware platform are given based on the obtained values. The complete system can be put together using inexpensive, off-the-shelf components, and perform autonomous localisation of discarded trash, relieving human personnel of this burdensome task, and enabling automated pickup planning.

Road Crack Detection based on Object Detection Algorithm using Unmanned Aerial Vehicle Image

2019 ◽  
Vol 18 (6) ◽  
pp. 155-163
Author(s):  
Jeong Min Kim ◽  
◽  
Se Gwon Hyeon ◽  
Jung Hwan Chae ◽  
Myung Sik Do
Keyword(s):  
Object Detection ◽  
Unmanned Aerial Vehicle ◽  
Crack Detection ◽  
Detection Algorithm ◽  
Aerial Vehicle

scholarly journals Hue–saturation–intensity and texture feature-based cloud detection algorithm for unmanned aerial vehicle images

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142090353
Author(s):  
Wang Yi ◽  
Zhang Jing ◽  
Gao Shuang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Target Identification ◽  
False Positive Rate ◽  
Texture Feature ◽  
Detection Algorithm ◽  
Cloud Detection ◽  
Detection Algorithms ◽  
Saturation Intensity ◽  
Positive Rate ◽  
Aerial Vehicle

There are a large number of cloud-covered areas in most unmanned aerial vehicle images and lead to the loss of information in the image and affect image post procession such as image fusion and target identification. Finding the cloud-occluded area in an image is a key step in image processing. Based on the differences of color and texture characteristics between cloud and ground, a cloud detection algorithm for the unmanned aerial vehicle images is proposed. Simulation results show that the proposed algorithm is better than the classical cloud detection algorithms in accuracy rate, false-positive rate, and kappa coefficient.

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