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基于水下机器人与数字图像技术的混凝土结构表面裂缝检测方法

谢文高 张怡孝 刘爱荣 傅继阳 胡晓勇 陈炳聪 袁向荣

谢文高, 张怡孝, 刘爱荣, 傅继阳, 胡晓勇, 陈炳聪, 袁向荣. 基于水下机器人与数字图像技术的混凝土结构表面裂缝检测方法[J]. 工程力学, 2022, 39(S): 64-70. doi: 10.6052/j.issn.1000-4750.2021.05.S010
引用本文: 谢文高, 张怡孝, 刘爱荣, 傅继阳, 胡晓勇, 陈炳聪, 袁向荣. 基于水下机器人与数字图像技术的混凝土结构表面裂缝检测方法[J]. 工程力学, 2022, 39(S): 64-70. doi: 10.6052/j.issn.1000-4750.2021.05.S010
XIE Wen-gao, ZHANG Yi-xiao, LIU Ai-rong, FU Ji-yang, HU Xiao-yong, CHEN Bing-cong, YUAN Xiang-rong. METHOD FOR CONCRETE SURFACE CRACKING DETECTION BASED ON ROV AND DIGITAL IMAGE TECHNOLOGY[J]. Engineering Mechanics, 2022, 39(S): 64-70. doi: 10.6052/j.issn.1000-4750.2021.05.S010
Citation: XIE Wen-gao, ZHANG Yi-xiao, LIU Ai-rong, FU Ji-yang, HU Xiao-yong, CHEN Bing-cong, YUAN Xiang-rong. METHOD FOR CONCRETE SURFACE CRACKING DETECTION BASED ON ROV AND DIGITAL IMAGE TECHNOLOGY[J]. Engineering Mechanics, 2022, 39(S): 64-70. doi: 10.6052/j.issn.1000-4750.2021.05.S010

基于水下机器人与数字图像技术的混凝土结构表面裂缝检测方法

doi: 10.6052/j.issn.1000-4750.2021.05.S010
基金项目: 国家自然科学基金项目(51878188); 高等学校学科创新引智计划项目(111计划D21021);广州市科技计划项目(20212200004)
详细信息
    作者简介:

    谢文高(1995−),男,广东人,硕士,主要从事水下机器人和图像处理研究(E-mail: 544869735@qq.com)

    张怡孝(1996−),女,河南人,硕士,主要从事桥梁静力稳定性研究(E-mail: zhangyixiao0226@163.com)

    傅继阳(1976−),男,湖北人,研究员,博士,主要从事工程结构抗风研究(E-mail: jyfu@gzhu.edu.cn)

    胡晓勇(1981−),男,山西人,高工,硕士,主要从事桥梁水下结构检测实践研究(E-mail: 46364767@qq.com)

    陈炳聪(1980−),男,广东人,高级实验师,博士,主要从事装配式组合梁研究(E-mail: bc_chen@gzhu.edu.cn)

    袁向荣(1957−),男,河北人,教授,博士,博导,主要从事结构检测的数字图像技术研究(E-mail: rongxyuan@163.com)

    通讯作者:

    刘爱荣(1972−),女,山西人,教授,博士,博导,主要从事桥梁稳定性研究(E-mail: liuar@gzhu.edu.cn)

  • 中图分类号: U446;TP391.4;TP242

METHOD FOR CONCRETE SURFACE CRACKING DETECTION BASED ON ROV AND DIGITAL IMAGE TECHNOLOGY

  • 摘要: 人工检测是目前桥梁水下结构病害检测的主要方式,存在着危险性高、效率低、准确性低等问题。针对以上问题,该文提出了采用搭载高清水下摄像头的水下机器人检测方法。该文在清水环境实验中对混凝土结构进行表观检测,通过图像处理、高斯滤波以及梯度计算的分权阈值法对存在裂缝的图像进行分析,提取裂缝边缘特征,提出基于斜率计算的欧氏距离下裂缝宽度计算方法实现对裂缝宽度的分析计算。实验结果表明:该文实验中裂缝检测结果精度较高,且裂缝的宽度参数计算精度可达规范要求。
  • 图  1  双目水下机器人

    Figure  1.  Binocular underwater vehicle

    图  2  机器人检测轨迹

    Figure  2.  Detection trajectory of ROV

    图  3  灰度图像

    Figure  3.  Gray image

    图  4  canny算子边缘提取流程

    Figure  4.  Edge extraction process of canny operator

    图  5  采用双阈值判断前

    Figure  5.  Before using double threshold judgment

    图  6  采用双阈值判断后

    Figure  6.  After double threshold judgment

    图  7  裂缝边缘识别图

    Figure  7.  Crack edge identification diagram

    图  8  裂缝边缘拾取图

    Figure  8.  Pick up drawing of crack edge

    图  9  裂缝宽度值 /mm

    Figure  9.  Value of crack width

    图  10  裂缝边缘拟合图

    Figure  10.  Crack edge fitting diagram

    图  11  裂缝宽度值变化图 /mm

    Figure  11.  Variation diagram of crack width

    表  1  裂缝宽度计算值与真实值对比

    Table  1.   Comparison between calculated value and real value of crack width

    样本编号计算宽度/mm实际宽度/mm误差/mm相对误差/(%)
    最小值最大值最小值最大值最小值最大值
    10.2730.604 0.260.610.0130.0062.99
    20.0943.2820.103.130.0060.1525.43
    30.2241.1690.211.130.0140.0395.06
    40.251.4280.261.360.0100.0684.42
    50.2660.8390.280.800.0140.0394.94
    60.1362.4560.132.420.0060.0363.05
    70.2762.010.261.940.0160.0704.88
    80.1980.7080.180.690.0180.0186.30
    90.1520.6000.130.600.0220.0008.46
    100.2150.8860.200.900.0150.0144.53
    110.3340.9840.321.020.0140.0363.95
    120.1841.1270.181.080.0040.0473.29
    130.2572.4820.262.460.0030.0221.02
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-05-29
  • 修回日期:  2022-03-02
  • 网络出版日期:  2022-04-09
  • 刊出日期:  2022-06-06

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