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dc.contributor.authorKuriakose, Bineeth
dc.contributor.authorShrestha, Raju
dc.contributor.authorSandnes, Frode Eika
dc.date.accessioned2022-03-30T13:17:46Z
dc.date.available2022-03-30T13:17:46Z
dc.date.created2022-01-24T11:08:34Z
dc.date.issued2022-01-06
dc.identifier.isbn978-1-6654-4207-7
dc.identifier.isbn978-1-6654-4208-4
dc.identifier.issn2577-1655
dc.identifier.issn1062-922X
dc.identifier.urihttps://hdl.handle.net/11250/2988634
dc.description.abstractDeep learning models have recently gained popularity in the research community due to their high classification success rates. In this paper, we proposed an EfficientNet-Lite based scene recognition model for scene recognition as a part of the smartphone-based navigation support application for the blind and visually impaired. We created a custom dataset with both indoor and outdoor scenes for training and testing of the model. The main objective of this work is to support people with visual impairments navigate by providing information about the scene via a smartphone application. The results from the experiment show encouraging performance from the proposed model. As a proof of concept, a prototype app was developed on the Android platform. However, the model can be implemented and deployed in any modern smartphone with good processing power.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.ispartofIEEE International Conference on Systems, Man and Cybernetics (SMC)
dc.relation.ispartofseriesIEEE International Conference on Systems, Man and Cybernetics;2021 IEEE International Conference on Systems, Man, and Cybernetics
dc.subjectScene recognitionen_US
dc.subjectVisual impairmentsen_US
dc.subjectNavigationen_US
dc.subjectDeep learningen_US
dc.subjectSmartphonesen_US
dc.subjectAssistive technologiesen_US
dc.titleSceneRecog: A Deep Learning Scene Recognition Model for Assisting Blind and Visually Impaired Navigate using Smartphonesen_US
dc.typeChapteren_US
dc.typePeer revieweden_US
dc.typeConference objecten_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.fulltextpostprint
cristin.qualitycode1
dc.identifier.doihttps://doi.org/10.1109/SMC52423.2021.9658913
dc.identifier.cristin1988381
dc.source.volume34en_US
dc.source.issue34en_US
dc.source.pagenumber7en_US


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