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Please use this identifier to cite or link to this item: https://digital.lib.ueh.edu.vn/handle/UEH/62248
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dc.contributor.authorDo T.H.-
dc.contributor.otherNguyen N.L.-
dc.contributor.otherVo H.T.-
dc.contributor.otherNguyen T.B.-
dc.contributor.otherVu Khanh N.T.-
dc.date.accessioned2021-09-05T02:41:44Z-
dc.date.available2021-09-05T02:41:44Z-
dc.date.issued2021-
dc.identifier.isbn9781665418430-
dc.identifier.urihttp://digital.lib.ueh.edu.vn/handle/UEH/62248-
dc.description.abstractAutonomous vehicle is the topic that gains a lot of attentions in recent years. To enable autonomous vehicle, a huge amount of data needs to be collected and processed. One of the most promising method to improve the accuracy of data collection is using vehicle communication. With the help of vehicle communication, controlling and warning signal can be transmitted between vehicles. Consequently, necessary information such as identity, position, speed, moving direction, future behavior can be obtained with higher accuracy. This paper focuses on a type of vehicle communication system called optical camera communication. One of the most important components of optical camera communication system is image processing. In this paper, a deep learning-based image processing algorithm is proposed for optical camera communication used for proactive data collecting system for autonomous vehicle. The experiment results show that the proposed algorithm can achieve much higher performance compared to its counterparts.en
dc.formatPortable Document Format (PDF)-
dc.language.isoeng-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.relation.ispartof2021 21st ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter)-
dc.rightsIEEE-
dc.subjectCameraen
dc.subjectCommunicationen
dc.subjectDeep learningen
dc.subjectDetectionen
dc.subjectLED regionen
dc.subjectVehicularen
dc.titleDeep learning based image processing for proactive data collecting system for autonomous vehicleen
dc.typeConference Paperen
dc.identifier.doihttps://doi.org/10.1109/SNPDWinter52325.2021.00062-
dc.format.firstpage253-
dc.format.lastpage256-
item.fulltextOnly abstracts-
item.languageiso639-1en-
item.openairetypeConference Paper-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
Appears in Collections:Conference Papers
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