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Object Detection and Mapping with Unmanned Aerial Vehicles Using Convolutional Neural Networks

  • Significant progress has been made in the field of deep learning through intensive research over the last decade. So-called convolutional neural networks are an essential component of this research. In this type of neural network, the mathematical convolution operator is used to extract characteristics or anomalies. The purpose of this work is to investigate the extent to which it is possible inSignificant progress has been made in the field of deep learning through intensive research over the last decade. So-called convolutional neural networks are an essential component of this research. In this type of neural network, the mathematical convolution operator is used to extract characteristics or anomalies. The purpose of this work is to investigate the extent to which it is possible in certain initial settings to input aerial recordings and flight data of Unmanned Aerial Vehicles (UAVs) in the architecture of a neural network and to detect and map an object. Using the calculated contours or dimensions of the so-called bounding boxes, the position of the objects can be determined relative to the current UAV location.zeige mehrzeige weniger

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Metadaten
Dokumentart:Konferenzveröffentlichung
Art der Konferenzveröffentlichung:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/5317
Bibliografische Angaben
Titel (Englisch):Object Detection and Mapping with Unmanned Aerial Vehicles Using Convolutional Neural Networks
Konferenzangaben:FABULOUS 2021: EAI International Conference (5. : May 6-7 2021 : Virtual Event)
Verfasserangaben:Stefan HenselStaff MemberORCiDGND, Marin B. MarinovORCiD, Max Schmitt
Erscheinungsjahr:2021
Verlagsort:Cham
Verlag:Springer
Seitenanzahl:13
Erste Seite:254
Letzte Seite:267
Titel des übergeordneten Werkes (Englisch):Future Access Enablers for Ubiquitous and Intelligent Infrastructures (FABULOUS 2021)
Jahrgang (Band):LNICST 382
ISBN:978-3-030-78458-4 (Print)
ISBN:978-3-030-78459-1 (Online)
ISSN:1867-8211 (Print)
ISSN:1867-822X (Online)
DOI:https://doi.org/10.1007/978-3-030-78459-1_19
Sprache:Englisch
Inhaltliche Informationen
Fakultäten / Einrichtungen:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Sammlungen der Hochschule Offenburg:Bibliografie
Formale Angaben
Open-Access-Status: Closed Access 
Lizenz (Deutsch):License LogoUrheberrechtlich geschützt