Research on Underwater Acoustic Target Recognition Method Based on DenseNet

Yao Yao, Xiangyang Zeng, Haitao Wang, Jie Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Under the statistical mode, underwater acoustic target recognition relies on heavy feature engineering, and the manually extracted features are sometimes not necessarily effective. At the same time, for confidentiality reasons, the lack of underwater acoustic data will also seriously affect the performance of the underwater acoustic target recognition system. In view of the above problems, the convolutional neural network, residual neural network and densely connected convolutional neural network are introduced and improved, and a Res-DenseNet-based network model is proposed and applied to the underwater acoustic target recognition task. An experimental study was carried out on the dataset. The experimental results show that, compared with the traditional method of MFCC+SVM, using the ResNet network alone and the DenseNet network alone, the correct recognition rates of the new model proposed in this paper are increased by 9.48%, 5.09% and 5.06%, respectively. The method in this paper can be effectively used for underwater acoustic target recognition.

Original languageEnglish
Title of host publication2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages114-118
Number of pages5
ISBN (Electronic)9781665451604
DOIs
StatePublished - 2022
Event3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022 - Virtual, Online, China
Duration: 15 Jul 202217 Jul 2022

Publication series

Name2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022

Conference

Conference3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022
Country/TerritoryChina
CityVirtual, Online
Period15/07/2217/07/22

Keywords

  • Convolutional Neural Network
  • Densely Connected Convolutional Neural Network
  • Residual Neural Network
  • Underwater Acoustic Target Recognition

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