TY - GEN
T1 - Research on Underwater Acoustic Target Recognition Method Based on DenseNet
AU - Yao, Yao
AU - Zeng, Xiangyang
AU - Wang, Haitao
AU - Liu, Jie
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Convolutional Neural Network
KW - Densely Connected Convolutional Neural Network
KW - Residual Neural Network
KW - Underwater Acoustic Target Recognition
UR - http://www.scopus.com/inward/record.url?scp=85146347130&partnerID=8YFLogxK
U2 - 10.1109/ICBAIE56435.2022.9985924
DO - 10.1109/ICBAIE56435.2022.9985924
M3 - 会议稿件
AN - SCOPUS:85146347130
T3 - 2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022
SP - 114
EP - 118
BT - 2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022
Y2 - 15 July 2022 through 17 July 2022
ER -