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基于GRU-CNN网络的隧道裂缝实时检测算法

文国军 高晓峰 毛宇 程斯一

文国军, 高晓峰, 毛宇, 程斯一. 基于GRU-CNN网络的隧道裂缝实时检测算法[J]. 地质科技通报, 2023, 42(6): 249-256. doi: 10.19509/j.cnki.dzkq.tb20220129
引用本文: 文国军, 高晓峰, 毛宇, 程斯一. 基于GRU-CNN网络的隧道裂缝实时检测算法[J]. 地质科技通报, 2023, 42(6): 249-256. doi: 10.19509/j.cnki.dzkq.tb20220129
Wen Guojun, Gao Xiaofeng, Mao Yu, Cheng Siyi. Real-time detection algorithm of tunnel cracks based on GRU-CNN[J]. Bulletin of Geological Science and Technology, 2023, 42(6): 249-256. doi: 10.19509/j.cnki.dzkq.tb20220129
Citation: Wen Guojun, Gao Xiaofeng, Mao Yu, Cheng Siyi. Real-time detection algorithm of tunnel cracks based on GRU-CNN[J]. Bulletin of Geological Science and Technology, 2023, 42(6): 249-256. doi: 10.19509/j.cnki.dzkq.tb20220129

基于GRU-CNN网络的隧道裂缝实时检测算法

doi: 10.19509/j.cnki.dzkq.tb20220129
基金项目: 

国家自然科学基金项目 41972325

国家自然科学基金项目 52205611

湖北省重点研发计划 2020BAB054

详细信息
    作者简介:

    文国军(1978—), 男, 教授, 主要从事机器视觉、智能定向钻进的研究与教学工作。E-mail: wenguojun@cug.edu.cn

    通讯作者:

    程斯一(1988—), 男, 副教授, 主要从事智能检测、工程机械设计的研究与教学工作。E-mail: chengsiyi@cug.edu.cn

  • 中图分类号: U456;TP391.41

Real-time detection algorithm of tunnel cracks based on GRU-CNN

  • 摘要:

    隧道裂缝严重损害隧道的使用寿命以及行车安全, 而传统人工检测方法无法对长隧道中的大量裂缝进行高效精确识别。提出了一种隧道表面裂缝实时检测算法, 该方法创新性地将用于文本学习、信号分析的门控循环单元(GRU)模型应用于图像分类中, 用于提升隧道裂缝检测速度并保证检测精度。为提高训练效率, 首先对裂缝进行预处理将其转换至频域中提取隧道裂缝的关键信息并矩阵重构为一维向量, 再利用一维卷积神经网络提取一维向量的深度特征并输入循环神经网络学习深度特征中的序列依存关系, 最终实现对隧道裂缝的检测。测试结果表明该模型不仅能降低模型训练参数量和硬件配置需求, 同时该模型在精度上能达到99.0%, 检测单张图片速度能达到2.1 s, 相较于主流的分类检测模型其准确率保持不变, 训练时间和预测速度显著提升。最后针对大尺寸隧道裂缝图像开发了检测框架, 可实现对大尺寸图像中裂缝信息的有效提取。

     

  • 图 1  GRU-CNN模型检测流程

    Figure 1.  Testing process of GRU-CNN model

    图 2  隧道裂缝预处理流程图

    Figure 2.  Preprocessing flow diagram

    图 3  2D图像频域信息转换

    a.转换前频域图; b.转换后频域图

    Figure 3.  2D image frequency domain transformation

    图 4  有无裂缝频域图对比

    a.裂缝图; b.裂缝频域线图; c.无裂缝图; d.无裂缝频域图

    Figure 4.  Comparison of frequency domain diagram with and without cracks

    图 5  高通滤波器前(a)、后(b)裂缝图片对比

    Figure 5.  Comparison of cracks images before (a) and after (b) high-pass filter

    图 6  GRU工作机制

    r.重置门控;z.更新门控;h.隐层单元;$\tilde{h} $.待更新隐层单元

    Figure 6.  Working mechanism of GRU

    图 7  GRU-CNN模型框架

    ReLU.激活函数; HPF.高通滤波; FFT.离散傅里叶变换

    Figure 7.  GRU-CNN framework

    图 8  模型损失loss和准确率accuracy变化图

    Figure 8.  Trends for both GRU-RNN loss and Accuracy

    图 9  裂缝检测框架

    Figure 9.  Crack detection framework

    图 10  训练不同阶段裂缝检测结果图(epoch代表训练轮次)

    Figure 10.  Crack detection results under different epochs

    表  1  GRU-CNN模型框架及参数

    Table  1.   Framework and parameters of GRU-CNN

    层类型 类型 输出形态 参数量
    Layer 1 Conv1d-1 [-1, 32, 4 096] 128
    BatchNorm1d-2 [-1, 32, 4 096] 64
    ReLU-3 [-1, 32, 4 096] 0
    MaxPool1d-4 [-1, 32, 2 047] 0
    Layer 2 Conv1d-5 [-1, 64, 2 047] 6 208
    BatchNorm1d-6 [-1, 64, 2 047] 128
    ReLU-7 [-1, 64, 2 047] 0
    MaxPool1d-8 [-1, 64, 1 022] 0
    Layer 3 Conv1d-9 [-1, 128, 1 022] 24 704
    BatchNorm1d-10 [-1, 128, 1 022] 256
    ReLU-11 [-1, 128, 1 022] 0
    MaxPool1d-12 [-1, 128, 255] 0
    Layer 4 Conv1d-13 [-1, 256, 255] 98 560
    BatchNorm1d-14 [-1, 256, 255] 512
    ReLU-15 [-1, 256, 255] 0
    MaxPool1d-16 [-1, 256, 63] 0
    Layer 5 Conv1d-17 [-1, 256, 63] 196 864
    BatchNorm1d-18 [-1, 256, 63] 512
    ReLU-19 [-1, 256, 63] 0
    MaxPool1d-20 [-1, 256, 15] 0
    Layer 6 GRU-21 [[-1, 256, 256], [4, -1, 256]] 1 393 920
    Layer 7 Dropout-22 [-1, 256] 0
    Linear-23 [-1, 256] 65 792
    ReLU-24 [-1, 256] 0
    Linear-26 [-1, 2] 514
    总参数: 张量(1 788 162)
    下载: 导出CSV

    表  2  评价指标混淆矩阵表示

    Table  2.   Evaluation for confusion matrix representation

    混淆矩阵 真实值
    裂缝 无裂缝
    预测值 裂缝 TP FP
    无裂缝 FN TN
    下载: 导出CSV

    表  3  主流模型对比实验结果

    Table  3.   Comparison of experimental result of mainstream models

    模型 准确率/% 训练时间/s 预测速度/(s·per-1)
    VGG 99.1 29 596 3.94
    GoogLeNet 99.5 11 580 2.65
    ResNet 97.5 17 582 3.01
    GRU-CNN 99.0 8 859 2.21
    下载: 导出CSV

    表  4  循环神经网络消融和替换实验

    Table  4.   Ablation and replacement of recurrent neural networks

    模型 准确率/% 训练时间/s 预测速度/(s·per-1) 参数量
    1D-CNN 98.5 8 305 2.21 858 882
    LSTM-CNN 98.7 10 711 2.34 2 252 802
    GRU-CNN 99.0 8 859 2.21 1 788 162
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-25
  • 录用日期:  2022-07-07
  • 修回日期:  2022-07-02

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