NFE-YOLO: A Lightweight and Efficient Detection Network for Low, Slow, and Small Drones

Abstract

With the rapid advancement of drone technology, there has been a significant increase in the demand for detecting “Low, Slow, and Small” (LSS) drones. However, existing deep learning networks often overlook critical details of such small targets during feature extraction, leading to reduced detection accuracy. Furthermore, edge inference devices typically have limited computational power, which hampers real-time processing capabilities. To address these challenges, we propose a lightweight and efficient drone detection network called NFE-YOLO. This network introduces an efficient positive traffic channel attention module known as EOrthoNet and enhances small target detection capabilities by improving the model’s neck structure. Specifically, the neck incorporates partial Convolution and C3Faster modules to reduce model size while the EOrthoNet channel attention module improves feature extraction accuracy. To further enhance the model’s generalization ability, we present a cross-scenario visible-light “Low, Slow, and Small” drone dataset referred to as UESTC Anti-UAV. This dataset comprises a total of 10,099 images that have all been manually annotated with precision to address the issue of insufficient sample diversity in existing datasets. Experimental results demonstrate a significant improvement in detection accuracy across multiple datasets. NFE-YOLO achieves a mAP50 value of 0.987 on the UESTC Anti-UAV dataset representing a 2.3% improvement over YOLOv8n with a model size of only 3.72MB. The proposed method effectively enhances detection accuracy for LSS drones and is crucial for the development of lightweight drone detection models.

Keywords

• Drone detection
• small target
• lightweight
• EOrthoNet
• fasternet
• dataset