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基于孪生网络的高速公路雾天能见度识别方法

汤伟 方嘉楠 张龙 杨晓东 李国强

汤伟, 方嘉楠, 张龙, 杨晓东, 李国强. 基于孪生网络的高速公路雾天能见度识别方法[J]. 交通信息与安全, 2023, 41(4): 122-131. doi: 10.3963/j.jssn.1674-4861.2023.04.013
引用本文: 汤伟, 方嘉楠, 张龙, 杨晓东, 李国强. 基于孪生网络的高速公路雾天能见度识别方法[J]. 交通信息与安全, 2023, 41(4): 122-131. doi: 10.3963/j.jssn.1674-4861.2023.04.013
TANG Wei, FANG Jianan, ZHANG Long, YANG Xiaodong, LI Guoqiang. A Method for Measuring Visibility under Foggy Weather for Expressways Based on Siamese Network[J]. Journal of Transport Information and Safety, 2023, 41(4): 122-131. doi: 10.3963/j.jssn.1674-4861.2023.04.013
Citation: TANG Wei, FANG Jianan, ZHANG Long, YANG Xiaodong, LI Guoqiang. A Method for Measuring Visibility under Foggy Weather for Expressways Based on Siamese Network[J]. Journal of Transport Information and Safety, 2023, 41(4): 122-131. doi: 10.3963/j.jssn.1674-4861.2023.04.013

基于孪生网络的高速公路雾天能见度识别方法

doi: 10.3963/j.jssn.1674-4861.2023.04.013
基金项目: 

国家重点研发计划项目 2017YFC0803907

陕西重点研发计划项目 2022GY-335

详细信息
    通讯作者:

    汤伟(1971—),博士,教授. 研究方向:智能交通、交通安全等. E-mail:wtang906@163.com

  • 中图分类号: U491.5

A Method for Measuring Visibility under Foggy Weather for Expressways Based on Siamese Network

  • 摘要: 从监控视频中准确识别高速公路雾天能见度等级,对于高速公路智能监管具有重要意义。针对当前高速公路能见度识别方法存在的精度低、速率慢、泛化性弱等问题,研究了基于孪生网络的能见度识别方法,重点关注于图像特征提取模块和等级识别主干模块的优化。图像特征提取模块采用改进的VGG16网络作为骨干网络,为增强网络从图像全局信息中提取重要特征的能力,在VGG16网络的5个block中增加卷积块注意力机制,达到强调有效特征和抑制无用特征的作用;为提升网络的泛化能力和训练速率,在网络卷积层之后添加滤波器响应归一化层来消除各维数据之间的差别;为解决网络权重参数冗余问题、防止过拟合,采用全局平均池化将输出特征图直接压缩为1×1向量,代替VGG16网络中前2层全连接层。等级识别主干模块采用孪生网络作为主体框架,将图像特征提取模块提取的有效特征进行前向传播,使用对比损失函数的距离测量方法在高维空间中对比输入图像对的相似程度来进行雾能见度等级识别。实验以2022年8月—2023年1月通过高速公路摄像机采集的陕西省部分高速公路雾天真实图像作为测试集对模型进行验证,结果表明:所提方法的识别准确率为90.3%,相比于单一网络AlexNet,ResNet50,VGG16在准确率上分别提高20.4%,18.9%,18.0%,相比于以单一网络为基准构建的孪生网络模型Simaese-AlexNet,Simaese-ResNet50,Simaese-VGG16在准确率上分别提高16.2%,11.0%,5.4%。所提出的方法对高速公路雾天能见度识别具备较高的准确率,有助于提升高速公路雾天的智能监管能力。

     

  • 图  1  不同雾浓度下部分高速公路的监控图像

    Figure  1.  Surveillance images of some highways at different fog concentrations

    图  2  雾天能见度识别模型

    Figure  2.  Recognition model for visibility in foggy weather

    图  3  VGG16网络结构图

    Figure  3.  The structure diagram of VGG16 network

    图  4  Enhance-VGG网络结构图

    Figure  4.  Enhance-VGG network architecture

    图  5  CBAM注意力机制整体流程图

    Figure  5.  Overall flowchart of CBAM attention mechanism

    图  6  全连接层与全局平均池化对比

    Figure  6.  Comparison between fully-connected layer and global average pooling

    图  7  孪生网络架构

    Figure  7.  The architecture of siamese network

    图  8  损失函数与样本特征欧氏距离的关系

    Figure  8.  The relationship between loss function and euclidean distance of sample feature

    图  9  混淆矩阵

    Figure  9.  Confusion matrix

    图  10  消融实验准确率曲线

    Figure  10.  Accuracy curve of ablation experiments

    图  11  4种单一网络准确率和损失值曲线

    Figure  11.  Accuracy and loss value curves of 4 individual networks.

    图  12  4种孪生网络准确率和损失值曲线

    Figure  12.  Accuracy and loss value curves of 4 siamese networks

    图  13  8种网络模型实验对比结果

    Figure  13.  Comparison of experimental results for 8 network models

    图  14  识别结果混淆矩阵

    Figure  14.  Confusion matrix of recognition results

    图  15  4种孪生网络算法高速公路数据集检测结果对比

    Figure  15.  Comparison of detection results for 4 siamese network algorithms on highway datasets

    图  16  2023年5月9日上午07:26往西安西富高速公路团雾变化

    Figure  16.  Variation of agglomerate fog on Xi'an Xifu highway on May 9, 2023, at 07:26 AM

    表  1  能见度等级标准

    Table  1.   Visibility level standards

    能见度等级 能见度距离/m
    0级(浓雾) 0~50
    1级(重雾) >50~200
    2级(大雾) >200~500
    3级(轻雾) >500~1 000
    4级(无雾) >1 000
    下载: 导出CSV

    表  2  样本分布

    Table  2.   Sample distribution 单位: 张

    数据集 能见度等级 总计
    0级(浓雾) 1级(重雾) 2级(大雾) 3级(轻雾) 4级(无雾)
    训练集 880 892 909 907 912 4 500
    验证集 289 296 300 306 309 1 500
    测试集 288 294 303 310 305 1 500
    下载: 导出CSV

    表  3  消融实验结果

    Table  3.   Results of ablation experiment

    网络 准确率/%
    VGG16 72.3
    VGG16-CBAM 77.4
    VGG16-GAP 75.3
    VGG16-FRN 83.6
    Enhance-VGG 87.7
    下载: 导出CSV
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  • 收稿日期:  2023-03-22
  • 网络出版日期:  2023-11-23

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