YOLODrone+: improved YOLO architecture for object detection in UAV images
dc.contributor.author | Şahin, Ö. | |
dc.contributor.author | Özer, Sedat | |
dc.date.accessioned | 2023-08-11T12:07:25Z | |
dc.date.available | 2023-08-11T12:07:25Z | |
dc.date.issued | 2022 | |
dc.identifier.isbn | 978-166545092-8 | |
dc.identifier.uri | http://hdl.handle.net/10679/8640 | |
dc.identifier.uri | https://ieeexplore.ieee.org/document/9864746 | |
dc.description.abstract | The 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.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 2022 30th Signal Processing and Communications Applications Conference (SIU) | |
dc.rights | restrictedAccess | |
dc.title | YOLODrone+: improved YOLO architecture for object detection in UAV images | en_US |
dc.title.alternative | YOLODrone+: IHA görüntülerinde nesne tanıma için geliştirilmiş YOLO mimarisi | |
dc.type | Conference paper | en_US |
dc.publicationstatus | Published | en_US |
dc.contributor.department | Özyeğin University | |
dc.contributor.authorID | (ORCID 0000-0002-2069-3807 & YÖK ID 386309) Özer, Sedat | |
dc.contributor.ozuauthor | Özer, Sedat | |
dc.identifier.doi | 10.1109/SIU55565.2022.9864746 | en_US |
dc.subject.keywords | Deep learning | en_US |
dc.subject.keywords | Object detection | en_US |
dc.subject.keywords | UAV | en_US |
dc.identifier.scopus | SCOPUS:2-s2.0-85138701707 | |
dc.relation.publicationcategory | Conference Paper - International - Institutional Academic Staff |
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