中国科学院大学学报2025，Vol.42Issue(6)：823-831,9.DOI:10.7523/j.ucas.2024.012
基于双分支特征融合卷积神经网络的高分辨距离像船只目标识别
HRRP ship targets recognition based on double branches feature fusion convolutional neural network
摘要
Abstract
To improve the accuracy of radar high resolution range profile ship target recognition,a ship target recognition method based on dual-branch feature fusion convolutional neural network model is proposed.Two branches are designed to extract features at different levels.The method designs a stacked convolutional detail branch with reduced downsampling to extract high resolution local features of ships.The global branch is composed of a modular structure used to extract low resolution global attitude features of ships.Based on the dimensional changes of the feature map after passing through two branches,the two features are changed in size separately in the feature fusion module,and the features are fused with each other to output recognition results.The experimental results show that the proposed method has faster convergence,fewer parameters,and higher accuracy compared to traditional recognition methods,verifying its effectiveness in HRRP ship classification.关键词
船只目标识别/高分辨距离像/卷积神经网络/特征融合Key words
ship target recognition/high resolution range profile/CNN/feature fusion分类
信息技术与安全科学引用本文复制引用
朱思键,齐向阳,范怀涛..基于双分支特征融合卷积神经网络的高分辨距离像船只目标识别[J].中国科学院大学学报,2025,42(6):823-831,9.基金项目
中国科学院空天信息创新研究院科学与颠覆性技术项目(E2Z216010F)资助 （E2Z216010F）
