Multiple Object Detection and Tracking in Real-Time Aerial Imagery with Deep Learning Architectures

Authors : Betül Akyüz, Melih Bahadır, Özkan İnik

Pages : 162-172

View : 62 | Download : 128

Publication Date : 2025-07-31

Article Type : Research Paper

Abstract :Real-time multi-object detection and tracking is one of the main challenges in analysing aerial imagery from platforms such as unmanned aerial vehicles (UAVs) and satellite systems. Traditional tracking algorithms are inadequate especially in complex and dynamic environments, which necessitates the development of more powerful and flexible methods. This study proposes a deep learning based solution in line with the objectives of high extraction rate and sufficient accuracy. Within the scope of the study, the YOLOv11 model, which is a single-stage object detection architecture, is integrated with the ByteTrack algorithm for tracking the detected objects. This approach is evaluated in comparison with the two-stage Faster R-CNN architecture (MobileNetV3-large FPN backbone), which is known for its high accuracy in object detection. The training of the models was performed on a comprehensive dataset created by combining the challenging VisDrone and DOTA datasets containing objects of different sizes. The results show that the YOLOv11 model is more suitable for real-time applications due to its high speed inference capability, while the Faster R-CNN model provides more accurate detections despite its higher computational cost. The ByteTrack algorithm used in the tracking phase has increased the tracking accuracy by successfully identifying the detected objects. In this context, the advantages and limitations of both models are evaluated and it is concluded that the choice of model should be made according to the requirements of the targeted application.
Keywords : Çoklu Nesne Takibi, YOLOv11, Faster R-CNN, ByteTrack, Havasal Görüntüler

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