I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Comparing Deep Learning Performance for Aircraft Detection ..:
, In:
NAECON 2023 - IEEE National Aerospace and Electronics Conference
,
Vergara, Victor M.
;
Wojcik, Jeremy J.
;
Kain, Evan T.
. - p. 86-91 , 2023
Link:
https://doi.org/10.1109/NAECON58068.2023.10366019
RT T1 <cTitle>NAECON 2023 - IEEE National Aerospace and Electronics Conference</cTitle>: T1 <Title>Comparing Deep Learning Performance for Aircraft Detection in Satellite Imagery</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10366019&Exemplar=1&LAN=DE A1 Vergara, Victor M. A1 Wojcik, Jeremy J. A1 Kain, Evan T. A1 Lovelly, Tyler M. YR 2023 SN 2379-2027 K1 YOLO K1 Training K1 Deep learning K1 Analytical models K1 Annotations K1 Shape K1 Computational modeling SP 86 OP 91 LK http://dx.doi.org/https://doi.org/10.1109/NAECON58068.2023.10366019 DO https://doi.org/10.1109/NAECON58068.2023.10366019 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
