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红外弱光下多特征融合与注意力增强铁路异物检测

陈永 王镇 卢晨涛 张娇娇

陈永,王镇,卢晨涛,等. 红外弱光下多特征融合与注意力增强铁路异物检测[J]. 北京航空航天大学学报,2023,49(8):1884-1895 doi: 10.13700/j.bh.1001-5965.2021.0591
引用本文: 陈永,王镇,卢晨涛,等. 红外弱光下多特征融合与注意力增强铁路异物检测[J]. 北京航空航天大学学报,2023,49(8):1884-1895 doi: 10.13700/j.bh.1001-5965.2021.0591
CHEN Y,WANG Z,LU C T,et al. Detection of railway object intrusion under infrared low light based on multi-feature and attention enhancement network[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(8):1884-1895 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0591
Citation: CHEN Y,WANG Z,LU C T,et al. Detection of railway object intrusion under infrared low light based on multi-feature and attention enhancement network[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(8):1884-1895 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0591

红外弱光下多特征融合与注意力增强铁路异物检测

doi: 10.13700/j.bh.1001-5965.2021.0591
基金项目: 国家自然科学基金(61963023,61841303);兰州交通大学天佑创新团队(TY202003);兰州交通大学基础研究拔尖人才项目(2022JC36)
详细信息
    通讯作者:

    E-mail:edukeylab@126.com

  • 中图分类号: TP391.4

Detection of railway object intrusion under infrared low light based on multi-feature and attention enhancement network

Funds: National Natural Science Foundation of China (61963023,61841303); Tianyou Innovation Team of Lanzhou Jiaotong University (TY202003); Lanzhou Jiaotong University Basic Top-Notch Personnel Project (2022JC36)
More Information
  • 摘要:

    针对红外弱光环境下铁路异物检测时存在目标特征提取不充分、检测精度及实时性低的问题,在CenterNet目标检测模型的基础上,提出了一种红外弱光下多特征融合与注意力增强的无锚框异物检测深度学习模型。在红外目标多尺度特征提取的基础上,引入自适应特征融合(ASFF)模块,充分利用目标高层语义与底层细粒度特征信息,提升红外目标特征提取能力。通过提出的空洞卷积增强注意力模块(Dilated-CBAM)进行关键特征提取,扩大注意力模块感受野范围,克服了原始CenterNet卷积块感受野映射区域变窄、无法检测弱小目标的问题,提升了无锚框网络的检测精度。使用Smooth L1损失函数进行训练,克服了L1损失函数在网络训练过程收敛速度慢及训练不稳定解的问题。通过铁路红外数据集及现场实验测试,结果表明:所提方法较原始CenterNet模型平均检测精度提高了8.03%,检测框置信度提升了31.23%,平均检测速率是Faster R-CNN模型的9.6倍,所提方法在红外弱光环境下能够更加快速准确地检测出铁路异物,主客观评价均优于对比方法。

     

  • 图 1  CenterNet目标检测模型

    Figure 1.  CenterNet target detection model

    图 2  本文模型整体框架

    Figure 2.  The proposed model framework

    图 3  多尺度ASFF模块

    Figure 3.  Multiscale-ASFF model

    图 4  空洞卷积示意图

    Figure 4.  Schematic diagram of dilated convolution

    图 5  感受野增强注意力Dilated-CBAM模块

    Figure 5.  Dilated-CBAM model of receptive field enhancement

    图 6  热力图对比实验结果

    Figure 6.  Comparison experimental results of thermodynamic diagrams

    图 7  损失函数对比

    Figure 7.  Comparison of loss functions

    图 8  训练损失曲线

    Figure 8.  Training loss curves

    图 9  弱光环境下铁路异物侵限检测实验结果

    Figure 9.  Results of railway foreign object intrusion detection experiment in low light environment

    图 10  复杂现场铁路异物侵限检测实验结果

    Figure 10.  Results of railway foreign object intrusion detection experiment in complex environment

    图 11  不同方法ROC曲线比较

    Figure 11.  Comparison of ROC curves of different methods

    图 12  不同方法AUC值比较

    Figure 12.  Comparison of AUC value of different methods

    表  1  不同方法红外铁路侵限检测指标对比

    Table  1.   Comparison of infrared railway intrusion detection indicators of different methods

    方法mAP/%置信度/%平均检测速率/(帧·s−1
    文献[8]90.4895.45 1.66
    文献[9]92.3670.42 1.97
    CenterNet方法89.4742.3216.67
    本文方法97.5073.5515.98
    下载: 导出CSV

    表  2  本文模型消融实验指标比较

    Table  2.   Comparison of ablation experimental indexes in the proposed method

    基准ASFF
    模块
    Dilated-CBAM
    模块
    Smooth L1
    损失函数
    mAP/%
    89.47
    92.38
    95.66
    97.50
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-10-06
  • 录用日期:  2021-12-24
  • 网络出版日期:  2022-01-25
  • 整期出版日期:  2023-08-31

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