Drone Tracking with Drone using Deep Learning
Yazarlar (3)
Fırat Üniversitesi, Türkiye
Dr. Öğr. Üyesi Ziya TAN
Erzincan Binali Yıldırım Üniversitesi
| Makale Türü | Özgün Makale |
| Makale Alt Türü | Diğer hakemli uluslarası dergilerde yayınlanan tam makale |
| Dergi Adı | International Journal of Computer and Information Technology |
| Dergi ISSN | 2279-0764 |
| Dergi Tarandığı Indeksler | Google Scholar |
| Makale Dili | İngilizce |
| Basım Tarihi | 06-2022 |
| Cilt No | 11 |
| Sayı | 3 |
| Sayfalar | 84 / 90 |
| DOI Numarası | 10.24203/ijcit.v11i3.238 |
| Makale Linki | https://www.ijcit.com/index.php/ijcit/article/view/238/67 |
| Özet |
| With the development of technology, studies in fields such as artificial intelligence, computer vision and deep learning are increasing day by day. In line with these developments, object tracking and object detection studies have spread over wide areas. In this article, a study is presented by simulating two different drones, a leader and a follower drone, accompanied by deep learning algorithms. Within the scope of this study, it is aimed to perform a drone tracking with drone in an autonomous way. Two different approaches are developed and tested in the simulator environment within the scope of drone tracking. The first of these approaches is to enable the leader drone to detect the target drone by using object-tracking algorithms. YOLOv5 deep learning algorithm is preferred for object detection. A data set of approximately 2500 images was created for training the YOLOv5 algorithm. The Yolov5 object detection … |
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BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 8 |