基于一维注意力机制的卷积神经网络水声目标识别

张羽飞; 赵梅; 胡长青; 郭政

doi:10.11993/j.issn.2096-3920.2025-0053

基于一维注意力机制的卷积神经网络水声目标识别

doi: 10.11993/j.issn.2096-3920.2025-0053

张羽飞^{1, 2,},
赵梅^1,,
胡长青^1,,
郭政^1,

1.
中国科学院声学研究所东海研究站, 上海, 201815
2.
中国科学院大学, 北京, 100049

详细信息

作者简介:
张羽飞 (2000-), 男, 在读硕士, 主要研究方向为水声目标识别

中图分类号: TB566; TP183
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- 被引次数: 0
出版历程
- 收稿日期: 2025-04-07
- 修回日期: 2025-05-18
- 录用日期: 2025-05-20
- 网络出版日期: 2025-09-12

Based on One-dimensional Attention Mechanism Convolutional Neural Network for Underwater Acoustic Target Recognition

ZHANG Yufei^{1, 2
,},
ZHAO Mei^1
,,
HU Changqing^1
,,
GUO Zheng^1
,

1.
Shanghai Acoustics Laboratory, Chinese Academy of Sciences, Shanghai 201815, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

摘要

摘要: 针对基于深度学习的水声目标识别模型存在网络参数复杂、计算成本高等问题, 提出一种轻量级一维注意力机制卷积神经网络水声目标识别模型。首先, 在特征提取阶段, 选择频谱、梅尔谱、色度、谱对比度和色调特征, 将其重构并融合为一维混合特征。之后, 通过多尺度残差卷积(MRC)以增强混合特征在不同尺度上的特征表示。同时, 引入卷积注意力模块(CBAM), 通过通道注意力和空间注意力模块自适应地调整特征的重要性, 提升模型对关键区域的关注。实验结果表明, 该模型在ShipsEar数据集上的平均识别率达到98.58%, 表现出良好的分类效果, 且运算量大大减少。
- 水声目标识别 /
- 多特征融合 /
- 卷积注意力模块 /
- 一维神经网络
Abstract: To address the issues of complex network parameters and high computational costs in deep learning-based underwater acoustic target recognition models, this study proposes a lightweight one-dimensional convolutional neural network with an attention mechanism for underwater acoustic target recognition.First, during the feature extraction stage, spectral, Mel-spectrogram, chroma, spectral contrast, and tonal features are selected and reconstructed into a fused one-dimensional hybrid feature. Next, the hybrid feature is processed by a multi-scale residual convolution (MRC) module to enhance feature representation across different scales. Simultaneously, a Convolutional Block Attention Module (CBAM) is introduced to adaptively adjust feature importance through channel and spatial attention mechanisms, improving the model's focus on critical regions.Experimental results show that the proposed model achieves an average recognition accuracy of 98.58% on the ShipsEar dataset, demonstrating excellent classification performance. Compared to existing models, this model significantly reduces computational complexity. Further validation on real-world data from the East China Sea confirms its effectiveness.
- underwater acoustic target recognition /
- multi-feature fusion /
- convolutional block attention module /
- 1D neural network

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图 1 CBAM注意力机制模块结构

Figure 1. Structure of the CBAM attention mechanism module

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图 2 1D-MRC-CBAM模型

Figure 2. 1D-MRC-CBAM model

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图 3 卷积块的具体结构

Figure 3. The specific structure of a convolutional block

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图 4 识别流程图

Figure 4. Identify flow chart

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图 5 ShipsEar数据集训练上的每一轮的训练损失、识别率图

Figure 5. Training loss and recognition accuracy plots for each epoch on the ShipsEar dataset

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图 6 测试集输出可视化结果

Figure 6. Visualization results of test set outputs

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图 7 移除CBAM模块后模型训练过程

Figure 7. Training process of the model without CBAM module

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图 8 卷积核尺寸为1, 3, 5时模训练过程

Figure 8. Training process of the model with kernel sizes 1, 3, 5

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图 9 卷积核尺寸为5, 7, 9时模型训练过程

Figure 9. Training process of the model with kernel sizes 5, 7, 9

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图 10 1D-MRC-CBAM模型在东海数据上识别结果

Figure 10. 1D-MRC-CBAM model recognition results on East China Sea data

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表 1 各个模块的输出大小及运算量

Table 1. Output size and computational complexity of each module

模块	输入大小	输出大小	运算量/10³
A_Block	(1, 1, 2 000)	(1, 16, 500)	2.5
B_Block	(1, 1, 2 000)	(1, 16, 500)	4.1
C_Block	(1, 1, 2 000)	(1, 16, 500)	5.7
Concatenation	(1, 16, 31)	(1, 48, 31)	12.3
D_Block	(1, 48, 31)	(1, 16, 31)	4.3
Fc1	(1, 16, 31)	(1, 1, 256)	127
Fc2	(1, 1, 256)	(1, 1, 16)	54.1

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表 2 ShipsEar数据集的样本分类

Table 2. The dataset of ShipsEar

分类	船只种类	合计
A	挖泥船/渔船/贻贝船/拖网渔船/拖船	1 875
B	摩托艇/引航船/帆船	1 560
C	客船	4 270
D	邮轮/滚装船	2 455
E	自然噪声	1 140

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表 3 不同特征重构前与重构后的维度大小

Table 3. Dimensionality of features before and after reconstruction

特征	原始大小	重构后大小
FFT	1×1 000	1×1 000
MFCC	25×20	1×500
Chorma	12×20	1×240
Contrast	6×20	1×120
Tonnetz	6×20	1×120

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表 4 模型参数设置

Table 4. Parameterization of the model

参数选择	参数设置
学习率	10⁻³
迭代次数	100
优化器	Adam
损失函数	交叉熵损失函数
训练批次	64

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表 5 不同信噪比下模型的识别率

Table 5. Recognition rates of the model under different signal-to-noise ratios

信噪比/dB	A/%	B/%	C/%	D/%	E/%
5	97.01	95.88	97.28	99.18	100
0	93.40	93.03	95.62	98.74	97.87
−5	96.39	90.41	93.03	94.67	94.06
−10	88.06	77.70	85.45	95.45	93.85
−15	77.90	70.80	78.99	82.37	87.00
−20	64.24	52.94	74.07	66.80	80.86

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表 6 不同输入特征下模型识别率

Table 6. Model accuracy for different input features

输入特征	识别率/%
FFT	97.32
MFCC	97.43
FFT+MFCC	98.32
FMCCT	98.58

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表 7 不同模型的识别率和参数对比

Table 7. Comparison of Recognition Rates and Parameters of Different Methods

模型	输入特征	识别率/%	参数量/10⁶
文献[20]	Mel频谱	96.4%	0.47
文献[21]	SSA谱	98.6%	0.26
文献[22]	STFT谱	99.4%	5.47
文献[23]	Log-Mel序列	98.5%	3.5
文献[24]	原始信号	99.8%	0.61
1D-MRC-CBAM	FMCCT混合特征	98.58%	0.2

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