[1]王念滨,何鸣,王红滨,等.适用于水下目标识别的快速降维卷积模型[J].哈尔滨工程大学学报,2019,40(07):1327-1333.[doi:10.11990/jheu.201805113]
 WANG Nianbin,HE Ming,WANG Hongbin,et al.A fast reduced-dimension convolution model for underwater target recognition[J].hebgcdxxb,2019,40(07):1327-1333.[doi:10.11990/jheu.201805113]
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适用于水下目标识别的快速降维卷积模型(/HTML)
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《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
40
期数:
2019年07期
页码:
1327-1333
栏目:
出版日期:
2019-07-05

文章信息/Info

Title:
A fast reduced-dimension convolution model for underwater target recognition
作者:
王念滨1 何鸣12 王红滨1 周连科1 商晓宇3
1. 哈尔滨工程大学 计算机科学与技术学院, 黑龙江 哈尔滨 150001;
2. 黑龙江科技大学 计算机与信息工程学院, 黑龙江 哈尔滨 150022;
3. 北京电子工程总院, 北京 100854
Author(s):
WANG Nianbin1 HE Ming12 WANG Hongbin1 ZHOU Lianke1 SHANG Xiaoyu3
1. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China;
2. College of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin 150022, China;
3. Beijing General Institute of Electronic Engineering, Beijing 100854, China
关键词:
水下目标识别注意力模型快速降维卷积神经网络预处理矢量化水听器
分类号:
TP183
DOI:
10.11990/jheu.201805113
文献标志码:
A
摘要:
针对传统卷积神经网络在相对较小的数据集上训练容易过拟合的问题,本文提出一个适用于水下目标识别的快速降维卷积网络模型(FRD-CMA)。该模型基于卷积核与特征图对应关系描述模型在数据集上的注意力,并以此进行快速降维,从而降低模型在小数据集上应用时存在的过拟合风险。FRD-CMA模型支持水下目标辐射噪声的端到端处理,通过提取辐射噪声的声音特征并依照水听器的时序关系进行矢量化处理,可以保持模型源输入特征不被破坏。试验结果表明:相较于之前的水下目标识别任务,FRD-CMA模型识别率提高5%,且模型训练时间缩短30%。

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-05-28。
基金项目:国家自然科学基金项目(61772152,61502037);基础科研项目(JCKY2017604C010,JCKY2016206B001,JCKY2014206C002);技术基础项目(JSQB2017206C002).
作者简介:王念滨,男,教授,博士生导师;王红滨,男,副教授.
通讯作者:王红滨,E-mail:wanghongbin@hrbeu.edu.cn
更新日期/Last Update: 2019-07-04