Object Detection with YOLOv7 Model on Smart Mobile Devices

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dc.authoridKaradağ, Batuhan/0000-0002-4661-6607
dc.authorwosidKaradağ, Batuhan/AFC-2569-2022
dc.contributor.authorKaradag, Batuhan
dc.contributor.authorAri, Ali
dc.date.accessioned2024-08-04T21:00:04Z
dc.date.available2024-08-04T21:00:04Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe YOLOv7 model, which is one of the current object detection algorithms based on deep learning, achieved an average accuracy of 51.2% in the Microsoft COCO dataset, proving that it is ahead of other object detection methods. YOLO has been a preferred model for object detection problems in the commercial field since it was first introduced, due to its speed , accuracy. Generally, high-capacity hardware is needed to run deep learning-based systems. In this study, it is aimed to detect objects in smart mobile devices without using a graphic processor unit by activating the YOLOv7 model on the server in order to be able to detect objects in smart mobile devices, which have become one of the important tools of trade today. With the study, the YOLOv7 object detection algorithm has been successfully run on mobile devices with iOS operating system. In this way, an image taken on mobile devices or already in the gallery after any image is transferred to the server, it is ensured that the objects in the image are detected effectively in terms of accuracy and speed.en_US
dc.identifier.doi10.2339/politeknik.1296541
dc.identifier.endpage1214en_US
dc.identifier.issn1302-0900
dc.identifier.issn2147-9429
dc.identifier.issue3en_US
dc.identifier.startpage1207en_US
dc.identifier.urihttps://doi.org/10.2339/politeknik.1296541
dc.identifier.urihttps://hdl.handle.net/11616/103778
dc.identifier.volume26en_US
dc.identifier.wosWOS:001094013300018en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherGazi Univen_US
dc.relation.ispartofJournal of Polytechnic-Politeknik Dergisien_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectYOLOv7en_US
dc.subjectObject Detectionen_US
dc.subjectMobile Object Detectionen_US
dc.subjectMobile YOLOv7en_US
dc.titleObject Detection with YOLOv7 Model on Smart Mobile Devicesen_US
dc.typeArticleen_US

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