Research on Real-Time Object Detection and Tracking for UAV Surveillance Based on Deep Learning

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Abstract

To address the challenges of low accuracy and insufficient real-time performance in dynamic object detection for UAV surveillance, this paper proposes a novel tracking framework that integrates a lightweight improved YOLOv5s model with adaptive motion compensation. A UAV-view dynamic feature enhancement strategy is innovatively introduced, and a lightweight detection network combining attention mechanisms and multi-scale fusion is constructed. The robustness of tracking under motion blur scenarios is also optimized. Experimental results demonstrate that the proposed method achieves a mAP@0.5 of 68.2% on the VisDrone dataset and reaches an inference speed of 32 FPS on the NVIDIA Jetson TX2 platform. This significantly improves the balance between accuracy and efficiency in complex scenes, offering reliable technical support for real-time applications such as emergency response.

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