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Neuromorphic Hardware Accelerated Lane Detection System

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dc.contributor.authorKim, Shinwook-
dc.contributor.authorChang, Tae-Gyu-
dc.date.available2019-03-08T07:36:48Z-
dc.date.issued2017-12-
dc.identifier.issn1745-1361-
dc.identifier.urihttps://scholarworks.bwise.kr/cau/handle/2019.sw.cau/3580-
dc.description.abstractThis letter describes the development and implementation of the lane detection system accelerated by the neuromorphic hardware. Because the neuromorphic hardware has inherently parallel nature and has constant output latency regardless the size of the knowledge, the proposed lane detection system can recognize various types of lanes quickly and efficiently. Experimental results using the road images obtained in the actual driving environments showed that white and yellow lanes could be detected with an accuracy of more than 94 percent.-
dc.format.extent5-
dc.language영어-
dc.language.isoENG-
dc.publisherIEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG-
dc.titleNeuromorphic Hardware Accelerated Lane Detection System-
dc.typeArticle-
dc.identifier.doi10.1587/transinf.2017PAL0004-
dc.identifier.bibliographicCitationIEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, v.E100D, no.12, pp 2871 - 2875-
dc.description.isOpenAccessN-
dc.identifier.wosid000417990300014-
dc.identifier.scopusid2-s2.0-85038382172-
dc.citation.endPage2875-
dc.citation.number12-
dc.citation.startPage2871-
dc.citation.titleIEICE TRANSACTIONS ON INFORMATION AND SYSTEMS-
dc.citation.volumeE100D-
dc.type.docTypeArticle-
dc.subject.keywordAuthorlane detection-
dc.subject.keywordAuthorneuromorphic hardware-
dc.subject.keywordAuthorneural network-
dc.subject.keywordAuthorautonomous vehicle-
dc.subject.keywordPlusTRACKING-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryComputer Science, Software Engineering-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
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