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HIGH-SPEED DRONE DETECTION BASED ON YOLO-V8

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dc.contributor.authorKim, Jun-Hwa-
dc.contributor.authorKim, Namho-
dc.contributor.authorWon, Chee Sun-
dc.date.accessioned2024-08-08T08:31:28Z-
dc.date.available2024-08-08T08:31:28Z-
dc.date.issued2023-
dc.identifier.issn1520-6149-
dc.identifier.issn2379-190X-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/20594-
dc.description.abstractDetecting drones in a video is a challenging problem due to their dynamic movements and varying range of scales. Moreover, since drone detection is often required for security, it should be as fast as possible. In this paper, we modify the state-of-the-art YOLO-V8 to achieve fast and reliable drone detection. Specifically, we add Multi-Scale Image Fusion and P2 Layer to the medium-size model (M-model) of YOLO-V8. Our model was evaluated in the 6th WOSDETC challenge. © 2023 IEEE.-
dc.language영어-
dc.language.isoENG-
dc.publisherIEEE-
dc.titleHIGH-SPEED DRONE DETECTION BASED ON YOLO-V8-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1109/ICASSP49357.2023.10095516-
dc.identifier.scopusid2-s2.0-85177596046-
dc.identifier.wosid001549214005018-
dc.identifier.bibliographicCitationICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), v.2023-June-
dc.citation.titleICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)-
dc.citation.volume2023-June-
dc.type.docTypeProceedings Paper-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaAcoustics-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryAcoustics-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.subject.keywordAuthorDrone Detection-
dc.subject.keywordAuthorSmall-object detection-
dc.subject.keywordAuthorYOLO-V8-
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