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Small UAV Target Detection Algorithm Using the YOLOv8n-RFL Based on Radar Detection Technology.

Zhijun Shi¹, Zhiyong Lei¹

¹School of Mechatronic Engineering, Xi'an Technological University, Xi'an 710021, China.

Sensors (Basel, Switzerland)

|August 28, 2025

Summary

This summary is machine-generated.

This study introduces an improved YOLOv8 network for enhanced unmanned aerial vehicle (UAV) detection using radar range-Doppler data. The novel approach effectively identifies UAV targets from complex backgrounds, improving radar detection accuracy.

Keywords:

UAV YOLOv8 attention mechanism detection radar

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Area of Science:

Radar technology
Artificial intelligence
Signal processing

Background:

Unmanned aerial vehicles (UAVs) pose detection challenges for traditional radar systems.
Improving the accuracy and reliability of UAV detection is crucial for security and surveillance.

Purpose of the Study:

To enhance the detection and recognition rate of UAVs using radar technology.
To develop an improved YOLOv8 network for UAV target identification.

Main Methods:

Utilizing radar range-Doppler planar graphs derived from UAV echo signals as input.
Employing an improved YOLOv8n-RFL network with novel C2f-RVB, C2f-RVBE modules, and a feature semantic fusion module (FSFM).
Implementing a lightweight sharing detection head (LWSD) for feature recognition.

Main Results:

The improved YOLOv8 network effectively processes range-Doppler planar graphs.
Novel modules enhance the extraction of multi-scale UAV features from complex backgrounds.
The system demonstrates effective detection of UAV targets in collected echo data.

Conclusions:

The proposed improved YOLOv8 algorithm significantly enhances UAV detection capabilities using radar.
This method offers a robust solution for identifying UAV targets in challenging environments.