Volltext-Downloads (blau) und Frontdoor-Views (grau)
  • search hit 506 of 1253
Back to Result List

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.show moreshow less

Export metadata

Additional Services

Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/5317
Bibliografische Angaben
Title (English):Object Detection and Mapping with Unmanned Aerial Vehicles Using Convolutional Neural Networks
Conference:FABULOUS 2021: EAI International Conference (5. : May 6-7 2021 : Virtual Event)
Author:Stefan HenselStaff MemberORCiDGND, Marin B. Marinov, Max Schmitt
Year of Publication:2021
Place of publication:Cham
Publisher:Springer
Page Number:13
First Page:254
Last Page:267
Parent Title (English):Future Access Enablers for Ubiquitous and Intelligent Infrastructures (FABULOUS 2021)
Volume: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
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Institutes:Bibliografie
Formale Angaben
Open Access: Closed Access 
Licence (German):License LogoUrheberrechtlich geschützt