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

Title	YOLODrone+: improved YOLO architecture for object detection in UAV images
Author	Şahin, Ö., Özer, Sedat
Publication Date:	2022
Publication Place	- IEEE
Subject	Deep learning, Object detection, UAV
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-166545092-8
Record ID	3b371650-7ecd-4717-9c27-6c3299eb6aca
Library Location	Computer Science
Date	2022
Sample Text	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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Özyeğin University

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

Author Şahin, Ö., Özer, Sedat

Publication Date 2022

Publication Place - IEEE

Subject Deep learning, Object detection, UAV

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-166545092-8

Record ID 3b371650-7ecd-4717-9c27-6c3299eb6aca

Library Location Computer Science

Date 2022

Sample Text 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