Aerial Landscape Recognition via Multi-Input Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F21%3A00557774" target="_blank" >RIV/60162694:G43__/21:00557774 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60162694:G38__/21:00557774 RIV/00216305:26620/21:PU143713

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9502737" target="_blank" >http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9502737</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICMT52455.2021.9502749" target="_blank" >10.1109/ICMT52455.2021.9502749</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Aerial Landscape Recognition via Multi-Input Neural Network

Popis výsledku v původním jazyce

Throughout the last decade, the advancements in the hardware allow use for wider applications of the unmanned aerial vehicles (UAV). UAVs feature significant advantages in autonomous aerial landscape mapping and recognition (ALR) over traditional methods due to their high level of operationality and mission repeatability, along with a simple alteration of e.g., on board remote sensors. ALR system based on convolutional neural networks is proposed. The system is designed with real-time capabilities. Data classification based on histogram and Gabor filter is explored on commercially available aerial images. The research roadmap designed to offload the dependency of the process on flight testing to improve the cost-efficiency of the development is proposed as well.

Název v anglickém jazyce

Aerial Landscape Recognition via Multi-Input Neural Network

Popis výsledku anglicky

Throughout the last decade, the advancements in the hardware allow use for wider applications of the unmanned aerial vehicles (UAV). UAVs feature significant advantages in autonomous aerial landscape mapping and recognition (ALR) over traditional methods due to their high level of operationality and mission repeatability, along with a simple alteration of e.g., on board remote sensors. ALR system based on convolutional neural networks is proposed. The system is designed with real-time capabilities. Data classification based on histogram and Gabor filter is explored on commercially available aerial images. The research roadmap designed to offload the dependency of the process on flight testing to improve the cost-efficiency of the development is proposed as well.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

2021 8th International Conference on Military Technologies, ICMT 2021 - Proceedings

ISBN

978-1-6654-3724-0

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Piscataway, USA

Místo konání akce

Brno, the Czech Republic

Datum konání akce

8. 6. 2021

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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