EVALUATION OF THE EFFECTIVENESS OF MODERN OBJECT DETECTION MODELS ON SPATIAL IMAGE DATA

Tóm tắt

Object detection in aerial imagery, especially from unmanned aerial vehicles (UAVs), presents numerous challenges due to varying altitudes, occlusions, and diverse object scales—particularly the detection of small objects. This paper provides a comparative evaluation of three advanced object detection models: YOLOv11, RT-DETR, and RF-DETR, using the VisDrone2019 dataset, which includes complex urban and suburban scenes captured from UAVs. We analyze the models based on key performance metrics such as mean average precision (mAP), inference speed, model size, and computational complexity. Experimental results show that YOLOv11 achieves the highest processing speed, making it especially suitable for real-time applications due to its fast inference and strong edge-device performance. RF-DETR, on the other hand, achieves the best accuracy, with the fastest mAP@0.5 and mAP@[0.5:0.95] scores of 46.9% and 26.6%, respectively, demonstrating effectiveness in complex scenarios with high object density and occlusions. RT-DETR offers a balanced trade-off between speed and accuracy, making it a practical choice for applications requiring both responsiveness and reliable detection quality. These findings clarify the strengths and limitations of each model and provide practical guidance for selecting suitable object detection models in UAV-based surveillance and tracking tasks.