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Improving the Performance of Yolo-Ba...

Sahin, Oyku.

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Improving the Performance of Yolo-Based Detection Algorithms for Small Object Detection in Uav-Taken Images = = Kucuk Nesne Tanima uZerine Kullanilan Yolo Tabanli Nesne Tanima Algoritmalarinin Iyilestirilmesi.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Improving the Performance of Yolo-Based Detection Algorithms for Small Object Detection in Uav-Taken Images =/
其他題名:	Kucuk Nesne Tanima uZerine Kullanilan Yolo Tabanli Nesne Tanima Algoritmalarinin Iyilestirilmesi.
作者:	Sahin, Oyku.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	121 p.
附註:	Source: Masters Abstracts International, Volume: 84-10.
Contained By:	Masters Abstracts International84-10.
標題:	Cameras. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30394584
ISBN:	9798377692768

Improving the Performance of Yolo-Based Detection Algorithms for Small Object Detection in Uav-Taken Images = = Kucuk Nesne Tanima uZerine Kullanilan Yolo Tabanli Nesne Tanima Algoritmalarinin Iyilestirilmesi.
Sahin, Oyku.

Improving the Performance of Yolo-Based Detection Algorithms for Small Object Detection in Uav-Taken Images =Kucuk Nesne Tanima uZerine Kullanilan Yolo Tabanli Nesne Tanima Algoritmalarinin Iyilestirilmesi. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 121 p.

Source: Masters Abstracts International, Volume: 84-10.

Thesis (M.S.)--Bilkent Universitesi (Turkey), 2023.

Recent advances in computer vision yield emerging novel applications for cameraequipped unmanned aerial vehicles such as object detection. The accuracy of the existing object detection solutions running on images acquired by Unmanned Aerial Vehicles (UAVs) is limited when compared to the performance of the object detection solutions designed for ground-taken images. Existing object detection solutions demonstrate lower performance on aerial datasets because of the reasons originating from the nature of the UAVs. These reasons can be summarized as: (i) the lack of large drone datasets with different types of objects, (ii) the larger variance in both scale and orientation of objects in drone images, and (iii) the difference in shape and texture of the features between the ground and the aerial images. Due to these reasons, YOLO-based models, a popular family of one-stage object detectors, perform insufficiently in UAV-based applications. In this thesis, two improved YOLO models: YOLODrone and YOLODrone+ are introduced for detecting objects in drone images. The performance of the models are tested on VisDrone2019 and SkyDataV1 datasets and improved results are reported when compared to the original YOLOv3 and YOLOv5 models.

ISBN: 9798377692768Subjects--Topical Terms:

524039
Cameras.

Improving the Performance of Yolo-Based Detection Algorithms for Small Object Detection in Uav-Taken Images = = Kucuk Nesne Tanima uZerine Kullanilan Yolo Tabanli Nesne Tanima Algoritmalarinin Iyilestirilmesi.
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520 $a Recent advances in computer vision yield emerging novel applications for cameraequipped unmanned aerial vehicles such as object detection. The accuracy of the existing object detection solutions running on images acquired by Unmanned Aerial Vehicles (UAVs) is limited when compared to the performance of the object detection solutions designed for ground-taken images. Existing object detection solutions demonstrate lower performance on aerial datasets because of the reasons originating from the nature of the UAVs. These reasons can be summarized as: (i) the lack of large drone datasets with different types of objects, (ii) the larger variance in both scale and orientation of objects in drone images, and (iii) the difference in shape and texture of the features between the ground and the aerial images. Due to these reasons, YOLO-based models, a popular family of one-stage object detectors, perform insufficiently in UAV-based applications. In this thesis, two improved YOLO models: YOLODrone and YOLODrone+ are introduced for detecting objects in drone images. The performance of the models are tested on VisDrone2019 and SkyDataV1 datasets and improved results are reported when compared to the original YOLOv3 and YOLOv5 models.
520 $a Bilgisayarli gorunun onemli alanlarindan biri olan nesne tanima alanindaki son gelismeler, kamera donanimli Insansiz Hava Araclari (IHA) icin ortaya cikan yeni cozumler saglar. Insansiz Hava Araclari'ndan alinan goruntuler uzerinde calisan, nesne tanima cozumlerinin basarimi, yerden alinan goruntuler icin nesne tanima cozumlerinin performansi ile karsilastirildiginda daha siniridir. Mev- cut nesne tanima cozumleri, IHA'larin dogasindan kaynaklanan nedenlerle IHA veri kumelerinde daha dusuk performans gostermektedir. Bu nedenler: (i) farkli nesne boyutlarina sahip buyuk drone veri setlerinin olmamasi, (ii) drone goruntulerinin heni olcek hem de yonelim olarak daha buyuk varyanslara sahip olmasi (iii) yer ve hava arasindaki sekil ve doku ozelliklerindeki farklilik. Bu sebeplerden dolayi, tek kademeli nesne tanima yontemlerinin bir parcasi olan YOLO tabanli modeller, IHA tabanli uygulamalarda daha dusuk performans gostermektedir. Bu tezde, YOLO algoritmasi uzerine YOLODrone ve YOLO- Drone, IHA goruntulerindeki nesneleri tanimak icin gelistirilmistir. Modellerin performansi VisDrone2019 ve SkyDataV1 veri ktimelerinde test edilip; YOLOv3 ve YOLOv5 ile karsilastirildiginda sonuclarda iyilestirme gozlemlenmistir.
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