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船舶辐射噪声分类卷积神经网络的可视化分析和卷积核剪枝

徐源超 蔡志明 孔晓鹏 黄炎

徐源超, 蔡志明, 孔晓鹏, 黄炎. 船舶辐射噪声分类卷积神经网络的可视化分析和卷积核剪枝[J]. 电子与信息学报, 2024, 46(1): 74-82. doi: 10.11999/JEIT230149
引用本文: 徐源超, 蔡志明, 孔晓鹏, 黄炎. 船舶辐射噪声分类卷积神经网络的可视化分析和卷积核剪枝[J]. 电子与信息学报, 2024, 46(1): 74-82. doi: 10.11999/JEIT230149
XU Yuanchao, CAI Zhiming, KONG Xiaopeng, HUANG Yan. Visualization Analysis and Kernel Pruning of Convolutional Neural Network for Ship-Radiated Noise Classification[J]. Journal of Electronics & Information Technology, 2024, 46(1): 74-82. doi: 10.11999/JEIT230149
Citation: XU Yuanchao, CAI Zhiming, KONG Xiaopeng, HUANG Yan. Visualization Analysis and Kernel Pruning of Convolutional Neural Network for Ship-Radiated Noise Classification[J]. Journal of Electronics & Information Technology, 2024, 46(1): 74-82. doi: 10.11999/JEIT230149

船舶辐射噪声分类卷积神经网络的可视化分析和卷积核剪枝

doi: 10.11999/JEIT230149
详细信息
    作者简介:

    徐源超:男,博士生,研究方向为水声目标识别

    蔡志明:男,教授,研究方向为水声信号与信息处理

    孔晓鹏:男,讲师,研究方向为水声目标识别与非线性声学

    黄炎:男,助教,研究方向为水声目标识别

    通讯作者:

    徐源超 xycwshr@126.com

  • 中图分类号: TN911.7; TP181; TB56

Visualization Analysis and Kernel Pruning of Convolutional Neural Network for Ship-Radiated Noise Classification

  • 摘要: 当前基于深度神经网络的船舶辐射噪声分类研究主要关注分类性能,对模型的解释性关注较少。本文首先采用导向反向传播和输入空间优化,基于DeepShip数据集,构建以对数谱为输入的船舶辐射噪声分类卷积神经网络(CNN),提出了一种船舶辐射噪声分类CNN的可视化分析方法。结果显示,多帧特征对齐算法改进了可视化效果,深层卷积核检测线谱和背景两类特征。其次,基于线谱是船舶分类的稳健特征这一知识,提出了一种卷积核剪枝方法,不仅提升了CNN分类性能,且训练过程更加稳定。导向反向传播可视化结果表明,卷积核剪枝后的CNN更加关注线谱信息。
  • 图  1  基于导向反向传播(GBP)和输入空间优化(OIS)的船舶辐射噪声CNN卷积核可视化流程

    图  2  CNN卷积核可视化分析各步骤输出

    图  3  Conv7中一个卷积核的可视化结果

    图  4  卷积核的4种典型模式

    图  5  线谱强度与分类正确率的关系

    图  6  卷积核剪枝前后CNN性能对比

    图  7  针对类别的GBP重建样本

    表  1  CNN模型参数

    层号参数
    0输入1×8×512维
    1Conv2d(16, 3×3)
    2Conv2d+MaxPool2d(32, 3×3), 2×2
    3Conv2d+MaxPool2d(64, 1×3), 2×2
    4Conv2d+MaxPool2d(64, 1×3), 2×2
    5Conv2d+MaxPool2d(64, 1×3), 1×2
    6Conv2d+MaxPool2d(64, 1×3), 1×2
    7Conv2d+MaxPool2d(128, 1×3), 1×2
    8FC256
    9FC64
    10FC4
    下载: 导出CSV

    表  2  DeepShip数据集样本划分

    船舶
    类别
    训练
    记录数
    测试
    记录数
    训练
    样本数
    测试
    样本数
    货轮(A)882176581922
    客轮(B)1533887202824
    油轮(C)1924890422006
    拖船(D)561381421970
    合计489120335628722
    下载: 导出CSV
    算法1 多帧特征对齐算法
     输入:$ {N} $列160个频点的特征${\boldsymbol{M} }_{160 \times N}$;
       最大迭代次数$i_{\max }$;
       每列的最大平移量$ s_{\max } $。
     初始化:$s_{j}=0, \quad j=1, 2,\cdots, N$
     for $i=1,2, \cdots, i_{\max}$
       for $ j=1,2, \cdots, N $
         计算${\boldsymbol{M}}_j$与${\boldsymbol{M}}_{-j}$的相关${\rm{corr} }\left({\boldsymbol{M}}_{-j}, {\boldsymbol{M}}_{j}\right)$
         确定${\boldsymbol{M}}_j$的平移量$ s \in \{-1,0,1\} $
         if ${\rm{abs}}(s_j+s )\le s_{\max}$
           ${\boldsymbol{M}}_j$平移s个单位(该列末端补零):${ {\rm{shift} } }\left({\boldsymbol{M}}_{j}, s\right)$
           $s_j \leftarrow s_j +s$
     输出:${\boldsymbol{M}}_{160 \times N} $
    注:${\boldsymbol{M}}_{j}$指${\boldsymbol{M}}_{160 \times N}$的第j列;${\boldsymbol{M}}_{-j}$指${\boldsymbol{M}}_{160 \times N}$的除第j列外其余列的平均值;它们都是160维的向量。
    下载: 导出CSV

    表  3  各CNN模型分类正确率(%)

    模型训练集测试集
    CNN-12898.9469.28
    CNN-L6498.6770.58
    CNN-B6498.7868.28
    CNN-6498.4869.05
    P-CNN-6498.3768.53
    下载: 导出CSV

    表  4  不同最大线谱强度(dB)下CNN模型分类正确率(%)

    模型<12.912.9~14.014.0~15.715.7~18.218.2~21.021.0~23.023.0~24.6>24.6
    CNN-12855.7165.7670.8473.5071.2870.9767.0857.14
    CNN-L6456.8164.8470.8174.3472.7373.3672.0059.19
    CNN-B6454.1465.2870.0671.8870.7269.4766.9056.85
    CNN-6454.0364.9969.1973.3371.1870.2468.9559.96
    P-CNN-6453.2662.6467.4172.3570.8071.3870.0260.37
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-03-13
  • 修回日期:  2023-06-12
  • 网络出版日期:  2023-06-19
  • 刊出日期:  2024-01-17

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