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dc.contributor.authorÖzen, S.
dc.contributor.authorKaya, Uygar
dc.contributor.authorSemiz, A.
dc.contributor.authorYalçın, M. F.
dc.contributor.authorÇelebi, A. T.
dc.date.accessioned2023-11-09T05:49:18Z
dc.date.available2023-11-09T05:49:18Z
dc.date.issued2023
dc.identifier.urihttp://hdl.handle.net/10679/8963
dc.identifier.urihttps://ieeexplore.ieee.org/document/10224048
dc.description.abstractIn this paper, lane type classification and lane distance measurement system are proposed for autonomous vehicles. In the proposed system, the perspective transformation method is applied to the image taken from the vehicle camera, so that a bird's-eye view is obtained and the parts with stripes are cropped from the image, and then a multi-class classification model is implemented using neural network-based architectures to determine the stripe type. In addition, with the proposed distance measurement system, the distance of the vehicle to the right and left lanes is calculated during autonomous driving, thus ensuring that the vehicle can drive autonomously on the lane. For this study, our own data set has been created.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartof2023 31st Signal Processing and Communications Applications Conference (SIU)
dc.rightsrestrictedAccess
dc.titleLane type classification & distance measurement system for autonomous vehicleen_US
dc.typeConference paperen_US
dc.publicationstatusPublisheden_US
dc.contributor.departmentÖzyeğin University
dc.identifier.wosWOS:001062571000252
dc.identifier.doi10.1109/SIU59756.2023.10224048en_US
dc.subject.keywordsCustom dataseten_US
dc.subject.keywordsDeep learningen_US
dc.subject.keywordsImage processingen_US
dc.subject.keywordsLane distance measurementen_US
dc.subject.keywordsLane type classificationen_US
dc.identifier.scopusSCOPUS:2-s2.0-85173434745
dc.contributor.ozugradstudentKaya, Uygar
dc.relation.publicationcategoryConference Paper - International - Institutional Undergraduate Student


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