Publication:
YOLODrone+: improved YOLO architecture for object detection in UAV images

dc.contributor.authorŞahin, Ö.
dc.contributor.authorÖzer, Sedat
dc.contributor.departmentComputer Science
dc.contributor.ozuauthorÖZER, Sedat
dc.date.accessioned2023-08-11T12:07:25Z
dc.date.available2023-08-11T12:07:25Z
dc.date.issued2022
dc.description.abstractThe performance of object detection algorithms running on images taken from Unmanned Aerial Vehicles (UAVs) remains limited when compared to the object detection algorithms running on ground taken images. Due to its various features, YOLO based models, as a part of one-stage object detectors, are preferred in many UAV based applications. In this paper, we are proposing novel architectural improvements to the YO-LOv5 architecture. Our improvements include: (i) increasing the number of detection layers and (ii) use of transformers in the model. In order to train and test the performance of our proposed model, we used VisDrone and SkyData datasets in our paper. Our test results suggest that our proposed solutions can improve the detection accuracy.en_US
dc.identifier.doi10.1109/SIU55565.2022.9864746en_US
dc.identifier.isbn978-166545092-8
dc.identifier.scopus2-s2.0-85138701707
dc.identifier.urihttp://hdl.handle.net/10679/8640
dc.identifier.urihttps://doi.org/10.1109/SIU55565.2022.9864746
dc.language.isoengen_US
dc.publicationstatusPublisheden_US
dc.publisherIEEEen_US
dc.relation.ispartof2022 30th Signal Processing and Communications Applications Conference (SIU)
dc.relation.publicationcategoryInternational
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsDeep learningen_US
dc.subject.keywordsObject detectionen_US
dc.subject.keywordsUAVen_US
dc.titleYOLODrone+: improved YOLO architecture for object detection in UAV imagesen_US
dc.title.alternativeYOLODrone+: IHA görüntülerinde nesne tanıma için geliştirilmiş YOLO mimarisi
dc.typeConference paperen_US
dspace.entity.typePublication
relation.isOrgUnitOfPublication85662e71-2a61-492a-b407-df4d38ab90d7
relation.isOrgUnitOfPublication.latestForDiscovery85662e71-2a61-492a-b407-df4d38ab90d7

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