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Using Synthetic Images to Evaluate and Improve Object Detection Neural Networks Performance on Aerial Image Datasets

Konen, Kai and Hecking, Tobias (2022) Using Synthetic Images to Evaluate and Improve Object Detection Neural Networks Performance on Aerial Image Datasets. International Journal of Semantic Computing, 16 (3), pp. 339-356. World Scientific. doi: 10.1142/S1793351X22420016. ISSN 1793-351X.

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Official URL: https://doi.org/10.1142/S1793351X22420016


Item URL in elib:https://elib.dlr.de/186808/
Document Type:Article
Title:Using Synthetic Images to Evaluate and Improve Object Detection Neural Networks Performance on Aerial Image Datasets
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Konen, KaiKai.Konen (at) dlr.dehttps://orcid.org/0000-0001-7957-4477
Hecking, TobiasTobias.Hecking (at) dlr.dehttps://orcid.org/0000-0003-0833-7989
Date:2022
Journal or Publication Title:International Journal of Semantic Computing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:16
DOI :10.1142/S1793351X22420016
Page Range:pp. 339-356
Publisher:World Scientific
ISSN:1793-351X
Status:Published
Keywords:UAV, Object Detection, Neural Networks, Aerial Images, Synthetic Images, Simulation
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Components and Systems
DLR - Research area:Aeronautics
DLR - Program:L CS - Components and Systems
DLR - Research theme (Project):L - Unmanned Aerial Systems
Location: Köln-Porz
Institutes and Institutions:Institute for Software Technology > Intelligent and Distributed Systems
Institute for Software Technology
Deposited By: Konen, Kai
Deposited On:11 Aug 2022 10:50
Last Modified:19 Aug 2022 09:32

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