Εμφάνιση απλής εγγραφής

dc.creatorDimas G., Diamantis D.E., Kalozoumis P., Iakovidis D.K.en
dc.date.accessioned2023-01-31T07:55:37Z
dc.date.available2023-01-31T07:55:37Z
dc.date.issued2020
dc.identifier10.3390/s20082385
dc.identifier.issn14248220
dc.identifier.urihttp://hdl.handle.net/11615/73308
dc.description.abstractEvery day, visually challenged people (VCP) face mobility restrictions and accessibility limitations. A short walk to a nearby destination, which for other individuals is taken for granted, becomes a challenge. To tackle this problem, we propose a novel visual perception system for outdoor navigation that can be evolved into an everyday visual aid for VCP. The proposed methodology is integrated in a wearable visual perception system (VPS). The proposed approach efficiently incorporates deep learning, object recognition models, along with an obstacle detection methodology based on human eye fixation prediction using Generative Adversarial Networks. An uncertainty-aware modeling of the obstacle risk assessment and spatial localization has been employed, following a fuzzy logic approach, for robust obstacle detection. The above combination can translate the position and the type of detected obstacles into descriptive linguistic expressions, allowing the users to easily understand their location in the environment and avoid them. The performance and capabilities of the proposed method are investigated in the context of safe navigation of VCP in outdoor environments of cultural interest through obstacle recognition and detection. Additionally, a comparison between the proposed system and relevant state-of-the-art systems for the safe navigation of VCP, focused on design and user-requirements satisfaction, is performed. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.en
dc.language.isoenen
dc.sourceSensors (Switzerland)en
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85083816574&doi=10.3390%2fs20082385&partnerID=40&md5=12d54432cbe94df07bfc0d6f6eb8c4ea
dc.subjectDeep learningen
dc.subjectFuzzy logicen
dc.subjectObject recognitionen
dc.subjectObstacle detectorsen
dc.subjectRisk assessmenten
dc.subjectUncertainty analysisen
dc.subjectVisionen
dc.subjectAdversarial networksen
dc.subjectFuzzy logic approachen
dc.subjectLinguistic expressionsen
dc.subjectMobility restrictionsen
dc.subjectObstacle recognitionen
dc.subjectOutdoor environmenten
dc.subjectSpatial localizationen
dc.subjectState-of-the-art systemen
dc.subjectNavigationen
dc.subjectalgorithmen
dc.subjectfuzzy logicen
dc.subjecthumanen
dc.subjectmachine learningen
dc.subjectphysiologyen
dc.subjectuncertaintyen
dc.subjectvisionen
dc.subjectAlgorithmsen
dc.subjectFuzzy Logicen
dc.subjectHumansen
dc.subjectMachine Learningen
dc.subjectUncertaintyen
dc.subjectVisual Perceptionen
dc.subjectMDPI AGen
dc.titleUncertainty-aware visual perception system for outdoor navigation of the visually challengeden
dc.typejournalArticleen


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Εμφάνιση απλής εγγραφής