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Gabor滤波融合卷积神经网络的路面裂缝检测方法

陈晓冬 艾大航 张佳琛 蔡怀宇 崔克让

陈晓冬, 艾大航, 张佳琛, 蔡怀宇, 崔克让. Gabor滤波融合卷积神经网络的路面裂缝检测方法[J]. 中国光学, 2020, 13(6): 1293-1301. doi: 10.37188/CO.2020-0041
引用本文: 陈晓冬, 艾大航, 张佳琛, 蔡怀宇, 崔克让. Gabor滤波融合卷积神经网络的路面裂缝检测方法[J]. 中国光学, 2020, 13(6): 1293-1301. doi: 10.37188/CO.2020-0041
CHEN Xiao-Dong, AI Da-Hang, ZHANG Jia-Chen, CAI Huai-Yu, CUI Ke-Rang. Gabor filter fusion network for pavement crack detection[J]. Chinese Optics, 2020, 13(6): 1293-1301. doi: 10.37188/CO.2020-0041
Citation: CHEN Xiao-Dong, AI Da-Hang, ZHANG Jia-Chen, CAI Huai-Yu, CUI Ke-Rang. Gabor filter fusion network for pavement crack detection[J]. Chinese Optics, 2020, 13(6): 1293-1301. doi: 10.37188/CO.2020-0041

Gabor滤波融合卷积神经网络的路面裂缝检测方法

doi: 10.37188/CO.2020-0041
基金项目: 天津市交通运输科技发展项目(No. 2019-03)
详细信息
    作者简介:

    陈晓冬(1975—),男,浙江温州人,博士,教授,博士生导师,1996年、2002年于天津大学分别获得硕士、博士学位,主要从事光电成像与检测技术方向的研究。E-mail:xdchen@tju.edu.cn

    艾大航(1996—),男,山西晋中人,硕士研究生,2018年于天津大学获得学士学位,主要从事智能交通图像处理技术的研究。E-mail:aidh@tju.edu.cn

  • 中图分类号: TP391.4

Gabor filter fusion network for pavement crack detection

Funds: Transportation Technology Development Project of Tianjin (No. 2019-03)
More Information
  • 摘要: 针对神经网络难以从数据量较少、较单一的路面裂缝图像中提取有效特征的局限性,设计了一种融合Gabor滤波器的卷积神经网络模型(Gabor Filter Convolutional Neural Network,GF-CNN)以进行路面裂缝检测。GF-CNN模型首先将路面图像输入小型参数预测网络中,依据输入图像自适应地选取Gabor滤波器组的参数,并根据所预测参数构建滤波器组对初始路面图像进行滤波,得到Gabor纹理特征图。将纹理特征图输入至基于残差网络构建的特征分类网络中提取深层特征,判断其是否包含裂缝。在GAPs路面图像数据集上的测试结果表明,GF-CNN模型的F1分数达到0.7137,优于其他路面图像检测方法。该模型通过融合纹理特征改善CNN特征提取能力,同时降低Gabor滤波器参数敏感性以提高模型泛化能力,对于路面裂缝图像具有良好的适用性。
  • 图  1  AlexNet学习的第一层卷积层参数及Gabor滤波器组

    Figure  1.  Parameters of the first convolutional layer of AlexNet filters and Gabor filters

    图  2  GF-CNN网络结构图

    Figure  2.  GF-CNN network structure

    图  3  特征分类子网络结构

    Figure  3.  Sub-network structure of feature classification

    图  4  完整路面图像检测结果

    Figure  4.  Detection results of the complete road image

    图  5  路面图像块检测结果

    Figure  5.  Detection results of the road image blocks

    表  1  Gabor参数预测网络结构

    Table  1.   Prediction network structure of Gabor parameter

    名称类型步长/像素输出特征图尺寸/pixel特征图通道数
    输入层64×641
    卷积层Conv 7×7164×648
    激活层ReLU64×648
    最大池化层2×2232×328
    卷积层Conv 3×3132×3216
    激活层ReLU32×3216
    最大池化层2×2216×1616
    卷积层Conv 3×3116×1632
    激活层ReLU16×1632
    最大池化层2×228×832
    全连接层96
    激活层Sigmoid96
    下载: 导出CSV

    表  2  裂缝检测模型对比实验结果

    Table  2.   Comparison of experimental results of crack detection models

    精度召回率F1
    CrackIT0.46940.53940.4882
    ASINVOS0.61130.49940.5497
    ResNet0.68370.57500.6246
    GF-CNN0.80030.64410.7137
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-03-16
  • 修回日期:  2020-05-07
  • 网络出版日期:  2020-10-22
  • 刊出日期:  2020-12-01

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