Airspace Object Detection Above the Guarded Area Using Segmentation Neural Network
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F21%3A39918536" target="_blank" >RIV/00216275:25530/21:39918536 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-89880-9_22" target="_blank" >http://dx.doi.org/10.1007/978-3-030-89880-9_22</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-89880-9_22" target="_blank" >10.1007/978-3-030-89880-9_22</a>
Alternative languages
Result language
angličtina
Original language name
Airspace Object Detection Above the Guarded Area Using Segmentation Neural Network
Original language description
With the increasing number of drones and unmanned aerial vehicles (UAVs), more emphasis is placed on guarding of the airspace around private and also public buildings. In this contribution authors are introducing a complex multi-step approach for aerial objects detection. Introduced process is composed of a few consecutive steps, where objects are cropped from original input with use of cropping pattern provided by task of image segmentation. These objects are then classified and evaluated as a threat or not. However, the emphasis here is placed on the segmentation part only. Neural network topology, adopted from U-Net architecture, was proposed. Case study was made and discussed in an effort to cover a large number of possible states. The results of a proposed convolutional neural network architecture were compared with the U-Net architecture. Applying of the convolutional neural network to the task of airspace object detection lead to sufficiently precise results, thanks to which it is possible to assume the possibility of its use in the proposed multi-step detection system in further work.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF17_049%2F0008394" target="_blank" >EF17_049/0008394: Cooperation in Applied Research between the University of Pardubice and companies, in the Field of Positioning, Detection and Simulation Technology for Transport Systems (PosiTrans)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the Future Technologies Conference (FTC) 2021.Volume 2
ISBN
978-3-030-89879-3
ISSN
2367-3370
e-ISSN
2367-3389
Number of pages
10
Pages from-to
283-292
Publisher name
Springer Nature Switzerland AG
Place of publication
Cham
Event location
Online
Event date
Oct 28, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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