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频谱池化与混洗注意力增强的铁路异物轻量检测

陈永 王镇 张娇娇

陈永, 王镇, 张娇娇. 频谱池化与混洗注意力增强的铁路异物轻量检测[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20220074
引用本文: 陈永, 王镇, 张娇娇. 频谱池化与混洗注意力增强的铁路异物轻量检测[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20220074
CHEN Yong, WANG Zhen, ZHANG Jiaojiao. Lightweight Detection of Railway Object Intrusion Based on Spectral Pooling and Shuffled-Convolutional Block Attention Module Enhancement[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20220074
Citation: CHEN Yong, WANG Zhen, ZHANG Jiaojiao. Lightweight Detection of Railway Object Intrusion Based on Spectral Pooling and Shuffled-Convolutional Block Attention Module Enhancement[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20220074

频谱池化与混洗注意力增强的铁路异物轻量检测

doi: 10.3969/j.issn.0258-2724.20220074
基金项目: 国家自然科学基金项目(62462043,61963023)
详细信息
    作者简介:

    陈永(1979—),男,教授,博士,研究方向为计算机视觉与目标检测,E-mail:edukeylab@126.com

  • 中图分类号: TP391.4;TP183

Lightweight Detection of Railway Object Intrusion Based on Spectral Pooling and Shuffled-Convolutional Block Attention Module Enhancement

  • 摘要:

    针对红外弱光环境下铁路异物侵限检测时存在检测精度低、难以实现轻量化实时检测等问题,提出一种注意力增强的轻量化铁路异物检测方法. 首先,采用深度可分离卷积改进Darknet53特征提取网络,轻量化提取红外弱光场景下的铁路异物特征;其次,利用语义引导的红外频谱池化进行特征增强,提升红外图像下采样的特征质量;然后,提出混洗注意力机制(shuffled-convolutional block attention module, shuffled-CBAM),实现对关键红外目标的特征提取与融合,提高网络对红外目标检测的精度;最后,采用无锚框轻量化网络完成铁路异物侵限检测输出,克服锚框检测非极大值抑制操作实时性差的缺点,减小计算量的同时提高检测效率. 实验结果表明:所提轻量化模型具有较高检测精度,同较改进前模型尺寸减小179.01 MB,检测速率提升至39 帧/s,为YOLOv4方法的3.9倍;相较于对比检测方法,本文所提方法能够快速精确地检测出红外铁路异物.

     

  • 图 1  所提方法整体框架

    Figure 1.  Framework of proposed method

    图 2  深度可分离卷积结构

    Figure 2.  Depthwise separable convolution

    图 3  基于语义引导的红外频谱特征增强模块

    Figure 3.  Infrared spectral feature enhancement module based on semantic guidance

    图 4  红外特征图像下采样对比实验

    Figure 4.  Comparison experiments of infrared feature image downsampling

    图 5  通道混洗结构

    Figure 5.  Channel shuffle mechanism

    图 6  结合通道混洗的注意力机制结构

    Figure 6.  Shuffled-CBAM mechanism

    图 7  热力图可视化实验

    Figure 7.  Heat map visualization experiment

    图 8  红外近远景多目标侵限铁路实验

    Figure 8.  Infrared near and long-range railway multi-object intrusion detection experiment

    图 9  红外多类别异物侵限铁路实验

    Figure 9.  Infrared multi-category railway object intrusion experiment

    图 10  复杂场景下红外铁路异物侵限检测实验

    Figure 10.  Infrared railway object intrusion detection experiment in complex scenes

    表  1  不同异物检测方法性能对比

    Table  1.   Performance comparison of different object intrusion detection methods

    实验方法 模型大小/MB 平均检测
    精度/%
    FPS/(帧·s−1
    文献[10] 92.62 74.25 24
    文献[11] 244.26 82.62 10
    本文 42.49 80.25 39
    下载: 导出CSV

    表  2  所提方法分类性能实验

    Table  2.   Experiments on classification performance of proposed method

    异物种类 准确率/% 召回率/% F1-Score
    自行车 99.36 97.74 0.9854
    铁路 93.83 93.91 0.9387
    行人 83.16 83.67 0.8341
    卡车 76.45 79.26 0.7783
    汽车 77.86 77.21 0.7753
    石块 70.47 70.53 0.7050
    下载: 导出CSV

    表  3  模型计算量对比实验

    Table  3.   Comparative experiment of model calculation load

    实验方法 文献[10] 文献[11] 本文
    计算量 176.38 90.58 69.98
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-01-24
  • 修回日期:  2022-08-22
  • 网络出版日期:  2024-09-21

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