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.…
Document Type: | Conference Proceeding |
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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) |
Collections of the Offenburg University: | Bibliografie | Formale Angaben |
Open Access: | Closed Access |
Licence (German): | ![]() |