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    Challenges and Advances in UAV-Based Vehicle Detection Using YOLOv9 and YOLOv10
    (Institute of Electrical and Electronics Engineers Inc., 2024) Bakirci, Murat; Dmytrovych, Petro; Bayraktar, Irem; Anatoliyovych, Oleh
    Aerial imaging and object detection with unmanned aerial vehicle (UAV) systems present unique challenges, including varying altitudes, dynamic backgrounds, and changes in lighting and weather conditions. These factors complicate the detection process, demanding robust and adaptive algorithms. Furthermore, the need for real-time processing in UAV applications imposes stringent requirements on computational efficiency and resource management. This study presents a comparative analysis of two cutting-edge object detection algorithms, YOLOv9 and YOLOv10, specifically tailored for vehicle detection in UAVcaptured traffic images. Leveraging a custom dataset derived from UAV aerial imaging, both algorithms were trained and evaluated to assess their performance in terms of speed and accuracy. The experimental results reveal that while YOLOv9 demonstrates a marginally superior inference speed, YOLOv10 excels slightly in detection accuracy. © 2024 IEEE.
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    Multi-Class Vehicle Detection and Classification with YOLO11 on UAV-Captured Aerial Imagery
    (Institute of Electrical and Electronics Engineers Inc., 2024) Bakirci, Murat; Dmytrovych, Petro; Bayraktar, Irem; Anatoliyovych, Oleh
    Aerial imaging and object detection using unmanned aerial vehicle (UAV) systems pose unique challenges, including varying altitudes, dynamic backgrounds, and changes in lighting and weather conditions. These factors complicate the detection process, demanding robust and adaptive algorithms. Furthermore, the need for real-time processing in UAV applications imposes stringent requirements on computational efficiency and resource management. This study presents a comparative analysis of the cutting-edge object detection algorithm YOLO11, specifically tailored for vehicle detection in UAV-captured traffic images. Using a custom dataset derived from UAV aerial imaging, the algorithm was trained and evaluated to assess both its performance in terms of speed and accuracy, and the results were compared with YOLOv10. Experimental findings indicate that while YOLOv10 achieves slightly faster inference speeds, YOLO11 offers marginally better detection accuracy. © 2024 IEEE.