Object Detection using YOLOv3

Jamal Agha, Sana

Bitte benutzen Sie diese Kennung, um auf die Ressource zu verweisen: http://dx.doi.org/10.25673/80067

Langanzeige der Metadaten

DC Element	Wert	Sprache
dc.contributor.referee	Prof. Dr. Schenke, Michael	-
dc.contributor.referee	Prof. Dr. Hartmann, Karsten	-
dc.contributor.author	Jamal Agha, Sana	-
dc.date.accessioned	2022-03-31T06:42:55Z	-
dc.date.available	2022-03-31T06:42:55Z	-
dc.date.issued	2021-11-04	-
dc.date.submitted	2021-09-16	-
dc.identifier.uri	https://opendata.uni-halle.de//handle/1981185920/82021	-
dc.identifier.uri	http://dx.doi.org/10.25673/80067	-
dc.description.abstract	The aim of this work is to provide an overview of artificial neural networks and methods for object recognition within an image. Many methods will be thoroughly explained to gain perspective about the best approach to implement the image object detection system. Transfer learning should be used to keep track of the number of images required to train a small network. The results obtained can thus be compared and discussed.	eng
dc.language.iso	eng	-
dc.publisher	Hochschulbibliothek, Hochschule Merseburg	-
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	-
dc.subject	methods for object recognition	eng
dc.subject	artificial neural networks	eng
dc.subject	Transfer learning	eng
dc.subject.ddc	DDC::0** Informatik, Informationswissenschaft, allgemeine Werke::00* Informatik, Wissen, Systeme::006 Spezielle Computerverfahren	-
dc.title	Object Detection using YOLOv3	eng
dc.type	Bachelor Thesis	-
dc.identifier.urn	urn:nbn:de:gbv:542-1981185920-820219	-
local.versionType	submittedVersion	-
local.publisher.universityOrInstitution	Hochschule Merseburg	-
local.openaccess	true	-
local.accessrights.dnb	free	-
Enthalten in den Sammlungen:	Ingenieur- und Naturwissenschaften

Dateien zu dieser Ressource:

Datei	Beschreibung	Größe	Format
JamalAghaSana_Object Detection using YOLOv3.pdf		26.26 MB	Adobe PDF	Öffnen/Anzeigen

Zur Kurzanzeige BibTeX EndNote