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*

doi:10.7523/j.ucas.2024.012

摘要/Abstract

摘要： 为了提高雷达高分辨距离像船只目标识别准确率,本文提出了一种基于双分支特征融合卷积神经网络的船只目标识别方法。设计了两个提取不同层次特征的分支。设计堆叠卷积层且降采样次数少的细节分支,以提取船只的高分辨率局部特征;使用模块化结构组成全局分支,提取船只的低分辨率全局姿态特征。根据特征图通过两个分支后的维度变化,在特征融合模块中将两种特征分别改变大小,相互融合并输出识别结果。实验结果显示,本文提出的方法与传统识别方法相比,收敛更快、参数量较少且具有更高的准确性,验证了其在HRRP船只分类问题上的有效性。

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

Abstract: To improve the accuracy of radar high resolution range profile ship target recognition, a ship target recognition method based on double branches feature fusion convolutional neural network model was proposed. Two branches were designed to extract features at different levels. The method designs a stacked convolutional detail branches with fewer downsampling times 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.

Key words: ship target recognition, high resolution range profile, CNN, feature fusion

中图分类号:

TN957.52

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, DOI: 10.7523/j.ucas.2024.012.

ZHU Sijian, QI Xiangyang, FAN Huaitao. HRRP ship targets recognition based on double branches feature fusion convolutional neural network[J]. Journal of University of Chinese Academy of Sciences, DOI: 10.7523/j.ucas.2024.012.

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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

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