摘要
海洋船舶目标识别在民用和军事领域有着重要的战略意义,本文针对可见光图像和红外图像提出了一种基于注意力机制的双流对称特征融合网络模型,以提升复杂感知环境下船舶目标综合识别性能.该模型利用双流对称网络并行提取可见光和红外图像特征,通过构建基于级联平均融合的多级融合层,有效地利用可见光和红外两种模态的互补信息获取更加全面的船舶特征描述.同时将空间注意力机制引入特征融合模块,增强融合特征图中关键区域的响应,进一步提升模型整体识别性能.在VAIS实际数据集上进行系列实验证明了该模型的有效性,其识别精确度能达到87.24%,综合性能显著优于现有方法.
The marine ship target recognition has important strategic significance in civil and military fields,this paper proposes a two-stream symmetric feature fusion convolutional neural network model based on attention mechanism for visible and infrared images,in order to improve the comprehensive recognition performance of ship targets in complex perception environment.The model uses the two-stream symmetric network to extract visible and infrared image features in parallel.By constructing a multi-level fusion layer based on cascade average fusion,the complementary information of visible and infrared modes is effectively used to obtain more comprehensive ship feature description.At the same time,the spatial attention mechanism is introduced into the feature fusion module to enhance the response of key regions in the fusion feature map and further improve the overall recognition performance of the model.A series of experiments on the VAIS real data set have proved the effectiveness of the model,its recognition accuracy can reach 87.24%,and its comprehensive performance is significantly superior to the existing methods.
作者
孙祎芸
樊臻
董山玲
郑荣濠
兰剑
SUN Yi-yun;FAN Zhen;DONG Shan-ling;ZHENG Rong-hao;LAN Jian(College of Electrical Engineering,Zhejiang University,Hangzhou Zhejiang 310027,China;State Key Laboratory of Industrial Control Technology,Zhejiang University,Hangzhou Zhejiang 310027,China;School of Electronics and Information Engineering,Xi’an Jiaotong University,Xi’an Shaanxi 710049,China)
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2022年第11期2009-2018,共10页
Control Theory & Applications
基金
NSFC-浙江两化融合联合基金(U1809202)资助。
关键词
识别算法
特征级融合
卷积神经网络
空间注意力机制
recognition algorithm
feature level fusion
convolutional neural networks
spatial attention mechanism
