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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 被引量:1

Visualization Analysis and Kernel Pruning of Convolutional Neural Network for Ship-Radiated Noise Classification
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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。 Current research on the classification of ship-radiated noise utilizing deep neural networks primarily focuses on aspects of classification performance and disregards model interpretation.To address this issue,an approach involving guided backwardpropagation and input space optimization has been utilized to develop a Convolutional Neural Network(CNN)for ship-radiated noise classification.This CNN takes a logarithmic scale spectrum as input and is based on the DeepShip dataset,thus presenting a visualization method for ship-radiated noise classification.Results reveal that the multiframe feature alignment algorithm enhances the visualization effect,and the deep convolutional kernel detects two types of features:line spectrum and background.Notably,the line spectrum has been identified as a reliable feature for ship classification.Therefore,a convolutional kernel pruning method has been proposed.This approach not only enhances the performance of CNN classification,but also enhances the stability of the training process.The results of the guided backwardpropagation visualization suggest that the post-pruning CNN increasingly emphasizes the consideration of line spectrum information.
作者 徐源超 蔡志明 孔晓鹏 黄炎 XU Yuanchao;CAI Zhiming;KONG Xiaopeng;HUANG Yan(College of Electronic Engineering,Naval University of Engineering,Wuhan 430033,China)
出处 《电子与信息学报》 EI CAS CSCD 北大核心 2024年第1期74-82,共9页 Journal of Electronics & Information Technology
关键词 船舶辐射噪声分类 卷积神经网络 可视化分析 神经网络剪枝 导向反向传播 Ship-radiated noise classification Convolutional Neural Network(CNN) Visualization analysis Neural network pruning Guided backward propagation
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