航空科学技术2024，Vol.35Issue(2)：39-46,8.DOI:10.19452/j.issn1007-5453.2024.02.005
基于Swin Transformer和注意力机制的红外无人机检测算法
Infrared UAV Detection Algorithm Based on Swin Transformer and Attention Mechanism
摘要
Abstract
Infrared drone target detection has broad application prospects in both military and civilian fields,and it is a hot research topic in the field of computer vision.Due to the small scale of drone targets and the complex and ever-changing aerial environment,existing detection algorithms generally have low detection rates and high false alarm rates.Aiming at issues such as poor detection of infrared drone targets in complex scenarios,this article proposes a ST-YOLOA infrared unmanned aerial vehicle target detection model.Firstly,in order to improve model performance and effectively capture global information,an STCNet backbone feature extraction network is constructed using the Swin Transformer network architecture and coordinated attention(CA)mechanism;Secondly,in the feature fusion section,a PANet path aggregation network with residual structure is used to construct a feature pyramid to enhance the overall feature extraction ability,while improving the up and down sampling method to enhance detection ability;Finally,the decoupled detection head is used to predict the position of the drone target.The proposed model has a detection accuracy of 92.8%and a detection speed of 22frames/s,which is verified by experiments on an infrared drone dataset.This indicates that the model has better detection performance compared to other models,especially in complex environments,and basically meets the real-time detection requirements.It has practical significance for detection in multi drone target scenarios.关键词
红外无人机/目标检测/Swin Transformer/协调注意力机制/STCNetKey words
infrared unmanned aerial vehicle/target detection/Swin Transformer/coordinated attention mechanism/STCNet分类
信息技术与安全科学引用本文复制引用
王思宇,卢瑞涛,黄攀,杨小冈,夏文新,李清格,张震宇..基于Swin Transformer和注意力机制的红外无人机检测算法[J].航空科学技术,2024,35(2):39-46,8.基金项目
国家自然科学基金(62276274) （62276274）
航空科学基金(201851U8012) （201851U8012）
陕西省自然科学基金(2023-JC-YB-528)National Natural Science Foundation of China(62276274),Aeronautical Science Foundation of China(201851U8012),Shaanxi Natural Science Foundation(2023-JC-YB-528) （2023-JC-YB-528）
