空军工程大学学报2026，Vol.27Issue(3)：103-111,9.DOI:10.3969/j.issn.2097-1915.2026.03.011
基于频域增强与多尺度特征融合的无人机检测算法研究
A UAV Detection Based on Frequency Domain Enhancement and Multi-Scale Feature Fusion
胡星 1耿越鑫 2范东伟 2罗浩 2苏昊翔 2孙庆伟2
作者信息
- 1. 空军指挥学院研究生大队,北京,100086||93688部队,天津,300074
- 2. 93688部队,天津,300074
- 折叠
摘要
Abstract
In view of micro-small UAVs being met with a challenge in detecting against the complex background,this paper proposes an enhanced object detection framework based on the improved YOLOv12n(YOLO-Tiny-UAV).The algorithm is to introduce a wavelet transform-based frequency-domain feature decoupling module to strengthen feature extraction,construct a cascade detection head facing tiny UAVs to enhance feature representa-tion,and optimize bounding box regression accuracy in integration with a normalized Wasserstein distance(NWD)loss function.Meanwhile,a refined anti-UAV detection dataset(Anti-TinyUAV)is created,including multi-modal(infrared/visible light)data,multi-object configurations(single-target/swarm),and multi-aircraft labels(UAVs of 7 kinds),supporting intelligent decision-making for hierarchical countermeasure strategies.The experiments show that the model keeps an inference speed at 2.6 ms to achieve an mAP50 detection accuracy of 82.3％,an improve-ment of 5.65％over the baseline model,and a 4.58％increase at a recall rate for micro-small UAVs,providing an efficient solution for UAV monitoring in complex environments.关键词
反无人机/目标检测/YOLOv12/小波卷积Key words
anti-UAV/object detection/YOLOv12/wavelet convolution分类
信息技术与安全科学引用本文复制引用
胡星,耿越鑫,范东伟,罗浩,苏昊翔,孙庆伟..基于频域增强与多尺度特征融合的无人机检测算法研究[J].空军工程大学学报,2026,27(3):103-111,9.
