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Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/80067
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dc.contributor.refereeProf. Dr. Schenke, Michael-
dc.contributor.refereeProf. Dr. Hartmann, Karsten-
dc.contributor.authorJamal Agha, Sana-
dc.date.accessioned2022-03-31T06:42:55Z-
dc.date.available2022-03-31T06:42:55Z-
dc.date.issued2021-11-04-
dc.date.submitted2021-09-16-
dc.identifier.urihttps://opendata.uni-halle.de//handle/1981185920/82021-
dc.identifier.urihttp://dx.doi.org/10.25673/80067-
dc.description.abstractThe 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.isoeng-
dc.publisherHochschulbibliothek, Hochschule Merseburg-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectmethods for object recognitioneng
dc.subjectartificial neural networkseng
dc.subjectTransfer learningeng
dc.subject.ddcDDC::0** Informatik, Informationswissenschaft, allgemeine Werke::00* Informatik, Wissen, Systeme::006 Spezielle Computerverfahren-
dc.titleObject Detection using YOLOv3eng
dc.typeBachelor Thesis-
dc.identifier.urnurn:nbn:de:gbv:542-1981185920-820219-
local.versionTypesubmittedVersion-
local.publisher.universityOrInstitutionHochschule Merseburg-
local.openaccesstrue-
local.accessrights.dnbfree-
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