Airspace Object Detection Above the Guarded Area Using Segmentation Neural Network

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Airspace Object Detection Above the Guarded Area Using Segmentation Neural Network

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Airspace Object Detection Above the Guarded Area Using Segmentation Neural Network

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF17_049%2F0008394" target="_blank" >EF17_049/0008394: Spolupráce Univerzity Pardubice a aplikační sféry v aplikačně orientovaném výzkumu lokačních, detekčních a simulačních systémů pro dopravní a přepravní procesy (PosiTrans)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the Future Technologies Conference (FTC) 2021.Volume 2

ISBN

978-3-030-89879-3

ISSN

2367-3370

e-ISSN

2367-3389

Počet stran výsledku

10

Strana od-do

283-292

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Cham

Místo konání akce

Online

Datum konání akce

28. 10. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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