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基于双路神经网络融合模型的高速公路雾天检测

项煜 丛德铭 张洋 袁飞

项煜, 丛德铭, 张洋, 袁飞. 基于双路神经网络融合模型的高速公路雾天检测[J]. 西南交通大学学报, 2019, 54(1): 173-179. doi: 10.3969/j.issn.0258-2724.20180205
引用本文: 项煜, 丛德铭, 张洋, 袁飞. 基于双路神经网络融合模型的高速公路雾天检测[J]. 西南交通大学学报, 2019, 54(1): 173-179. doi: 10.3969/j.issn.0258-2724.20180205
XIANG Yu, CONG Deming, ZHANG Yang, YUAN Fei. Two-Stream Neural Network Fusion Model for Highway Fog Detection[J]. Journal of Southwest Jiaotong University, 2019, 54(1): 173-179. doi: 10.3969/j.issn.0258-2724.20180205
Citation: XIANG Yu, CONG Deming, ZHANG Yang, YUAN Fei. Two-Stream Neural Network Fusion Model for Highway Fog Detection[J]. Journal of Southwest Jiaotong University, 2019, 54(1): 173-179. doi: 10.3969/j.issn.0258-2724.20180205

基于双路神经网络融合模型的高速公路雾天检测

doi: 10.3969/j.issn.0258-2724.20180205
详细信息
    作者简介:

    项煜(1987—),男,博士研究生,研究方向为综合交通系统分析、交通信息化,E-mail: 409676667@qq.com

  • 中图分类号: V221.3

Two-Stream Neural Network Fusion Model for Highway Fog Detection

  • 摘要: 高速公路天气状况实时监察对于高速行车安全具备重要意义,然而气象检测只能对大范围区域的气象情况进行预报,不能满足高速行车各个路段气象情况实时检测的需求. 为此,提出一种基于双路神经网络融合模型的高速公路雾天检测算法. 该算法基于双路深度神经网络融合模型,提取雾天图像的可视深度图以及暗通道图像两种视觉特征,并利用深度神经网络进行建模,获得初步分类结果;然后,再利用均值融合层进行分数融合. 为了全面评测该算法的性能,构建了一个覆盖多个省份高速公路的视频监控雾天数据集(express way fog detection dataset,EWFD),该数据集能够全面涵盖国内高速公路的天气情况,并在该数据集上做了全面的分析对比实验. 实验结果显示,本文所提出的双路神经网络融合模型的雾天监测算法取得了93.7%的准确率,与国际前沿的检测分类算法101层残差网络(ResNet-101)相比,本文提出的算法准确率提高了10%以上.

     

  • 图 1  雾天检测双路神经网络融合模型

    Figure 1.  Two-stream neural network fusion model for fog detection

    图 2  暗通道示例

    Figure 2.  Dark channel examples

    图 3  深度图示例

    Figure 3.  Depth map examples

    图 4  深度残差网络

    Figure 4.  Deep residual network

    图 5  特征样本图

    Figure 5.  Sample feature map

    表  1  双路神经网络融合对比实验

    Table  1.   Experiments on two-stream neural network fusion

    特征 数据集 测试数 正确数 正确率/%
    RGB EWFD 4 000 3 292 82.3
    暗通道 EWFD 4 000 3 664 91.6
    深度图 EWFD 4 000 3 488 87.2
    暗通道+深度图 EWFD 4 000 3 748 93.7
    下载: 导出CSV

    表  2  现有方法对比实验

    Table  2.   Comparing with existing method

    方法 数据集 测试数 正确数 正确率/%
    AlexNet EWFD 4 000 3 502 76.3
    VGGNet-16 EWFD 4 000 3 184 79.6
    ResNet50 EWFD 4 000 3 288 82.3
    ResNet101 EWFD 4 000 3 308 82.7
    双路神经网络融合检测 EWFD 4 000 3 748 93.7
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-03-20
  • 修回日期:  2018-05-28
  • 网络出版日期:  2018-06-01
  • 刊出日期:  2019-02-01

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