Parking Space Occupancy Detection Using Deep Learning Methods

dc.authorwosidKARAKAYA, Murat/A-4952-2013
dc.contributor.authorAkinci, Fatih Can
dc.contributor.authorKarakaya, Murat
dc.contributor.otherComputer Engineering
dc.date.accessioned2024-10-06T10:58:20Z
dc.date.available2024-10-06T10:58:20Z
dc.date.issued2018
dc.departmentAtılım Universityen_US
dc.department-temp[Akinci, Fatih Can; Karakaya, Murat] Atilim Univ, Bilgisayar Muhendisligi, TR-06836 Ankara, Turkeyen_US
dc.description.abstractThis paper presents an approach for gathering information about the availabilty of the parking lots using Convoltional Neural Network (CNN) for image processing running on an embedded system. By using an efiicent neural network model, we made it possible to use a very low cost embedded system compared to the ones used in previous works on this topic. This efficient model's performance is compared to one of the models that proved its accuracy in image classification competitions. In these tests, we used datasets that has thousands of different images taken from parking lots in different light and weather conditions.en_US
dc.description.woscitationindexConference Proceedings Citation Index - Science
dc.identifier.citationcount0
dc.identifier.isbn9781538615010
dc.identifier.issn2165-0608
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.14411/8891
dc.identifier.wosWOS:000511448500602
dc.identifier.wosqualityN/A
dc.institutionauthorKarakaya, Kasım Murat
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEYen_US
dc.relation.ispartofseriesSignal Processing and Communications Applications Conference
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSmart Citiesen_US
dc.subjectComputer Visionen_US
dc.subjectDeep Learningen_US
dc.subjectConvolutional Neural Networksen_US
dc.titleParking Space Occupancy Detection Using Deep Learning Methodsen_US
dc.typeConference Objecten_US
dc.wos.citedbyCount0
dspace.entity.typePublication
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relation.isAuthorOfPublication.latestForDiscovery93f27ee1-19eb-42dc-b4eb-a3cc7dc4b057
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