Object Detection with YOLOv7 Model on Smart Mobile Devices
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Gazi Univ
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The 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.
Açıklama
Anahtar Kelimeler
YOLOv7, Object Detection, Mobile Object Detection, Mobile YOLOv7
Kaynak
Journal of Polytechnic-Politeknik Dergisi
WoS Q Değeri
Q4
Scopus Q Değeri
Cilt
26
Sayı
3
