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

Name: YOLODrone+: improved YOLO architecture for object detection in UAV images
Author: Şahin, Ö., Özer, Sedat

İsim	YOLODrone+: improved YOLO architecture for object detection in UAV images
Yazar	Şahin, Ö., Özer, Sedat
Basım Tarihi:	2022
Basım Yeri	- IEEE
Konu	Deep learning, Object detection, UAV
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-166545092-8
Kayıt Numarası	3b371650-7ecd-4717-9c27-6c3299eb6aca
Lokasyon	Computer Science
Tarih	2022
Örnek Metin	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.
DOI	10.1109/SIU55565.2022.9864746

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

Yazar Şahin, Ö., Özer, Sedat

Basım Tarihi 2022

Basım Yeri - IEEE

Konu Deep learning, Object detection, UAV

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-166545092-8

Kayıt Numarası 3b371650-7ecd-4717-9c27-6c3299eb6aca

Lokasyon Computer Science

Tarih 2022

Örnek Metin 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.

DOI 10.1109/SIU55565.2022.9864746