• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus 收录
  • 全国中文核心期刊
  • 中国科技论文统计源期刊
  • 中国科学引文数据库来源期刊

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
  • 孙刚,陈陶,李建林. 高速路雾天行车诱导系统道路能见度检测装置[J]. 科技通报,2015(12): 164-166 doi: 10.3969/j.issn.1001-7119.2015.12.060

    SUN Gang, CHEN Tao, LI Jianlin. Highway foggy road guidance system road visibility testing device[J]. Technology Bulletin, 2015(12): 164-166 doi: 10.3969/j.issn.1001-7119.2015.12.060
    包左军, 汤窃巧, 李长城, 等. 公路交通安全与气象影响[M]. 北京: 人民交通出版社, 2008: 57-63
    张巧汉, 何勇, 刘洪肩, 等. 商速公路雾区交通安全保暗技术[M]. 北京: 人民交通出版社, 2009: 38
    BRONTE S, BERGASA L M, ALCANTARILLA P F. Fog detection system based on computer vision techniques[C]//Intelligent Transportation Systems Conference. [S.l.]: IEEE, 2009: 1-6
    PAVLIC M, BELZNER H, RIGOLL G, et al. Image based fog detection in vehicles[C]//Proceedings of IEEE Intelligent Vehicles Symposium. [S.l.]: IEEE, 2012: 1132-1137
    TAN R T. Visibility in bad weather from a single image[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2008. [S.l.]: IEEE, 2008: 1-8
    HAUTIERE N, LABAYRADE R, AUBERT D. Real-time disparity contrast combination for onboard estimation of the visibility distance[J]. IEEE Transactions on Intelligent Transportation System, 2006, 7(2): 201-212 doi: 10.1109/TITS.2006.874682
    BUSCH C, DEBES E. Wavelet transform for analyzing fog visibility[J]. IEEE Intelligent Systems, 1998, 13(6): 66-71 doi: 10.1109/5254.736004
    SCHECHNER Y Y, NARASIMHAN S G, NAYAR S K. Polarization based vision through haze[J]. Appl. Opt., 2003, 42(3): 511-525 doi: 10.1364/AO.42.000511
    NARASIMHAN S G, NAYAR S K. Contrast restoration of weather degraded images[J]. IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25(6): 713-724 doi: 10.1109/TPAMI.2003.1201821
    NAYAR S K, NARASIMHAN S G. Vision in bad weather[J]. Proc. 7th IEEE ICCV, 1999, 2(6): 820-827
    NARASIMHAN S G, NAYAR S K. Removing weather effects from monochrome images[J]. Proc. IEEE Conf. CVPR, 2001, 2(6): 186-193
    ROBERT G H, MATTHEWS M P, PISANO P A. Automated extraction of weather variables from camera imagery[C]//Proceedings of the Mid-Continent Transportation Research Symposium. Ames: [s.n.], 2005: 1031-1043
    ROBERT G H, MICHAEL P M. Using camera imagery to measure visibility & fog[R]. Ames: For FHWA Presentation, 2001
    ROBERT G H, MICHAEL P M. Clarus research: visibility estimation from camera imagery[R]. Ames: For FHWA CLARUS Meeting, 2006
    BAUMER D, VERSICK S, VOGEL B. Determination of the visibility using a digital panorama camera[J]. Atmospheric Environment, 2008, 42: 2593-2602 doi: 10.1016/j.atmosenv.2007.06.024
    ZEILER M D, FERGUS R. Visualizing and understanding convolutional networks[C]//European Conference on Computer Vision. [S.l.]: Springer, 2014: 818-833
    REN S, CAO X, WEI Y, et al. Face alignment at 3000 FPS via regressing local binary features[C]//Computer Vision and Pattern Recognition. [S.l.]: IEEE, 2014: 1685-1692
    REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal network[C]//International Conference on Neural Information Processing Systems. Boston: MIT Press, 2015: 91-99
    SIMONYAN K, ZISSERMAN A. Two-stream convolutional networks for action recognition in videos[C]//International Conference on Neural Information Processing Systems. Boston: MIT Press, 2014: 568-576
    KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neuralnetworks[C]//Advances in Neural Information Processing Systems. [S.l.]: ACM, 2012: 1097-1105
    ZEILER M D, FERGUS R.Visualizing and understanding convolutional networks[C]//European Conference on Computer Vision. [S.l.]: Springer International Publishing, 2014: 818-833
    SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. [S.l.]: IEEE, 2015: 1-9
    HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Computer Vision and Pattern Recognition. [S.l.]: IEEE, 2016: 770-778
    HUANG G, LIU Z, MAATEN L V D, et al. Densely connected convolutional network[C]//IEEE Conference on Computer Vision and Pattern Recognition. [S.l.]: IEEE Computer Society, 2017: 2261-2269
    CAI B, XU X, JIA K, et al. DehazeNet:an end-to-end system for single image haze removal[J]. IEEE Transactions on Image Processing, 2016, 25(11): 5187-5198 doi: 10.1109/TIP.2016.2598681
    REN W, LIU S, ZHANG H, et al. Single image dehazing via multi-scale convolutional neural networks[M]//Computer Vision-ECCV 2016. [S.l.]: Springer International Publishing, 2016: 154-169
    HE K, SUN J, TANG X. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2011, 33(12): 2341-2353
    LAINA I, RUPPRECHT C, BELAGIANNIS V, et al. Deeper depth prediction with fully convolutional residual networks[C]//Fourth International Conference on 3D Vision. [S.l.]: IEEE, 2016: 239-248
    JIA, Y, SHELHAMER E, DONAHUE J, et al. Caffe: convolutional architecture for fast feature embeddi-ng[C]//Proceedings of the 22nd ACM international conference on Multimedia. Orlando: ACM, 2014: 675-678
  • 加载中
图(5) / 表(2)
计量
  • 文章访问数:  459
  • HTML全文浏览量:  255
  • PDF下载量:  13
  • 被引次数: 0
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

目录

    /

    返回文章
    返回