To address the limitations of traditional radar in detecting low-altitude small unmanned aerial vehicles (UAVs), a deep learning-based detection system for low-altitude small UAVs is proposed. The system is designed with a three-tier architecture: the perception layer uses cameras to capture real-time image data; the hardware layer, based on edge computing technology, employs the Jetson Nano as the hardware platform; and the decision layer integrates object detection algorithms to enable efficient and accurate early warning functionality. Through experimental comparison and analysis of mainstream object detection algorithms, the optimal algorithm, YOLOv8, is selected and optimized. Experimental results show significant improvements in various performance metrics of the optimized algorithm, validating the effectiveness of the proposed solution.
Key words
UAV /
deep learning /
edge computing /
Jetson Nano /
YOLOv8
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