Methodenvergleich zum Einsatz von Deep Learning zur kamerabasierten Ermittlung des Schneebedeckungsgrades

Perkovic, Mike

DC Field	Value	Language
dc.contributor.advisor	Dahlkemper, Jörg	-
dc.contributor.author	Perkovic, Mike	-
dc.date.accessioned	2024-03-22T14:31:16Z	-
dc.date.available	2024-03-22T14:31:16Z	-
dc.date.created	2022-01-03	-
dc.date.issued	2024-03-22	-
dc.identifier.uri	http://hdl.handle.net/20.500.12738/15307	-
dc.description.abstract	Diese Arbeit befasst sich mit der Frage, welche Methode dafür geeignet ist, den Schneebedeckungsgrad auf Bildern optimal zu erkennen. Zur Realisierung wird Deep Learning in Kombination mit Faltungsnetzen angewendet. Es werden verschiedene Bilddatensätze und Netze verwendet wie VGG16, Xception und DenseNet201.	de
dc.description.abstract	This thesis deals with the question of which method is suitable for optimally detecting the degree of snow cover on images. For the realization, Deep Learning in combination with convolutional networks is applied. Different image datasets and networks are used, such as VGG16, Xception and DenseNet201.	en
dc.language.iso	de	en_US
dc.subject	Kamera	en_US
dc.subject	Wetter	en_US
dc.subject	Schnee	en_US
dc.subject	Bedeckungsgrad	en_US
dc.subject	Deep Learning	en_US
dc.subject	Künstliches neuronales Netz	en_US
dc.subject	KNN	en_US
dc.subject	Erkennung	en_US
dc.subject	Algorithmus	en_US
dc.subject	Open Source	en_US
dc.subject	Python	en_US
dc.subject	OpenCV	en_US
dc.subject	TensorFlow	en_US
dc.subject	Keras	en_US
dc.subject	Neuronales Faltungsnetz	en_US
dc.subject	CNN	en_US
dc.subject	Camera	en_US
dc.subject	Weather	en_US
dc.subject	Snow	en_US
dc.subject	Coverage	en_US
dc.subject	Artificial Neural Network	en_US
dc.subject	ANN	en_US
dc.subject	Recognition	en_US
dc.subject	Algorithm	en_US
dc.subject	Convolutional Neural Network	en_US
dc.subject.ddc	004: Informatik	en_US
dc.title	Methodenvergleich zum Einsatz von Deep Learning zur kamerabasierten Ermittlung des Schneebedeckungsgrades	de
dc.type	Thesis	en_US
openaire.rights	info:eu-repo/semantics/openAccess	en_US
thesis.grantor.department	Fakultät Technik und Informatik	en_US
thesis.grantor.department	Department Informations- und Elektrotechnik	en_US
thesis.grantor.universityOrInstitution	Hochschule für Angewandte Wissenschaften Hamburg	en_US
tuhh.contributor.referee	Meisel, Andreas	-
tuhh.identifier.urn	urn:nbn:de:gbv:18302-reposit-183001	-
tuhh.oai.show	true	en_US
tuhh.publication.institute	Fakultät Technik und Informatik	en_US
tuhh.publication.institute	Department Informations- und Elektrotechnik	en_US
tuhh.type.opus	Masterarbeit	-
dc.type.casrai	Supervised Student Publication	-
dc.type.dini	masterThesis	-
dc.type.driver	masterThesis	-
dc.type.status	info:eu-repo/semantics/publishedVersion	en_US
dc.type.thesis	masterThesis	en_US
dcterms.DCMIType	Text	-
tuhh.dnb.status	domain	en_US
item.advisorGND	Dahlkemper, Jörg	-
item.creatorGND	Perkovic, Mike	-
item.languageiso639-1	de	-
item.cerifentitytype	Publications	-
item.openairecristype	http://purl.org/coar/resource_type/c_46ec	-
item.creatorOrcid	Perkovic, Mike	-
item.fulltext	With Fulltext	-
item.grantfulltext	open	-
item.openairetype	Thesis	-
Appears in Collections:	Theses