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基于多尺度注意力和空间通道重构卷积的冲击回波频谱图像分类
作者:
作者单位:

1.华北理工大学 人工智能学院;2.华北理工大学 冶金与能源学院

基金项目:

河北省自然科学(E2012209025) ；河北省创新能力提升计划项目(20557605D)；教育部产学合作协同育人项目(230806528022738)；华北理工大学研究生创新项目(2023047),


Impact echo spectral image classification based on multi-scale attention and spatial channel reconstruction convolution
Author:
Affiliation:

1.College of Artificial Intelligence,North China University of Science and Technology;2.College of Metallurgy and Energy,North China University of Science and Technology

Fund Project:

Natural Science Foundation of Hebei Province(E2012209025), Hebei Province Innovation Capability Improvement Project (20557605D), Ministry of Education industry-university cooperative education project(230806528022738), Graduate Innovation Project North China University of Science and Technology (2023047).

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    摘要:

    针对传统卷积神经网络对冲击回波信号频谱图像进行分类时，面临卷积神经网络特征提取能力不足和数据集类别不平衡的问题，提出一种基于多尺度注意力和空间通道重构卷积的神经网络模型（Multi-scale Hybrid Attention And Spatial Channel Reconstruction Convolutional Neural Networks，MHA-SCConvNet）。首先设计了多尺度混合注意力(Multi-scale Hybrid Attention，MHA)模块，用于提取不同尺度的频谱图像特征并增强模型对频谱波形关键信息的关注力度。其次，引入空间通道重构卷积(Spatial and Channel reconstruction Convolution，SCConv)模块，通过优化图像特征的表示来降低特征冗余。最后，提出了新的混合损失函数-梯度与分布协调边距损失（Gradient and Distribution Harmonized Margin Loss，GDHM Loss) ，该损失函数在动态情况下同时考虑难分类样本和少数类样本。在自建的数据集上进行了训练与测试，并与AlexNet、VGGNet、GoogLeNet等分类模型对比，MHA-SCConvNet准确率达到94.58%。实验结果表明，MHA-SCConvNet模型能够有效提高冲击回波信号频谱图像分类的准确率和效率。

    Abstract:

    Abstract: When traditional convolutional neural networks are used to classify the spectrum images of impact echo signals, they are faced with the problems of insufficient feature extraction ability, unbalanced data sets and small scale. This paper presents the Multi-scale Hybrid Attention and Spatial Channel Reconstruction Convolutional Neural Network (MHA-SCConvNet). The model initiates with a multi-scale hybrid attention module that extracts spectral features at diverse scales, significantly sharpening the focus on essential waveform information. This is followed by the integration of a Spatial and Channel Reconstruction Convolution (SCConv) module, designed to optimize the representation of image features and reduce redundancy effectively. Additionally, the paper introduces the Gradient and Distribution Harmonized Margin Loss (GDHM Loss), a dynamic hybrid loss function tailored to address the challenges of classifying both hard-to-classify and minority class samples. The MHA-SCConvNet was rigorously evaluated on a proprietary dataset, where it demonstrated a notable accuracy of 94.58%, outperforming established models such as AlexNet, VGGNet, and GoogLeNet. Experimental results validate the MHA-SCConvNet's superior capability in enhancing the accuracy and efficiency of impact echo spectral image classification.

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崔博,武冰冰,陈伟,孟庆洪,王晓,黄祺祥.基于多尺度注意力和空间通道重构卷积的冲击回波频谱图像分类[J].南京信息工程大学学报,,():

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  • 收稿日期:2024-07-19
  • 最后修改日期:2024-10-05
  • 录用日期:2024-10-08

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