高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于图像处理的建筑物振动位移测量算法

陈昌川 李奎 乔飞 姜宏伟 赵曼淇 公茂盛 王海宁 张天骐

陈昌川, 李奎, 乔飞, 姜宏伟, 赵曼淇, 公茂盛, 王海宁, 张天骐. 基于图像处理的建筑物振动位移测量算法[J]. 电子与信息学报, 2020, 42(10): 2516-2523. doi: 10.11999/JEIT190805
引用本文: 陈昌川, 李奎, 乔飞, 姜宏伟, 赵曼淇, 公茂盛, 王海宁, 张天骐. 基于图像处理的建筑物振动位移测量算法[J]. 电子与信息学报, 2020, 42(10): 2516-2523. doi: 10.11999/JEIT190805
Changchuan CHEN, Kui LI, Fei QIAO, Hongwei JIANG, Manqi ZHAO, Maosheng GONG, Haining WANG, Tianqi ZHANG. Measurement Algorithm of Building Vibration Displacement Based on Image Signal Processing[J]. Journal of Electronics and Information Technology, 2020, 42(10): 2516-2523. doi: 10.11999/JEIT190805
Citation: Changchuan CHEN, Kui LI, Fei QIAO, Hongwei JIANG, Manqi ZHAO, Maosheng GONG, Haining WANG, Tianqi ZHANG. Measurement Algorithm of Building Vibration Displacement Based on Image Signal Processing[J]. Journal of Electronics and Information Technology, 2020, 42(10): 2516-2523. doi: 10.11999/JEIT190805

基于图像处理的建筑物振动位移测量算法

doi: 10.11999/JEIT190805
基金项目: 国家重点研发计划(2017YFC1500601),国家自然科学基金(61671095, 61771085, 61702065, 61701067);重庆市研究生教育教学改革研究重点项目(yjg192019)
详细信息
    作者简介:

    陈昌川:男,1978年生,副教授,研究方向为智能信息处理、图像处理、移动通信

    李奎:男,1990年生,硕士生,研究方向为图像与信号处理、目标检测与识别

    乔飞:男,1977年生,副研究员,博士,研究方向为集成智能、信号处理

    姜宏伟:男,1996年生,硕士生,研究方向为图像处理

    赵曼淇:男,1997年生,博士生,研究方向为无人机目标检测追踪

    公茂盛:男,1976年生,研究员,博士,研究方向为地震工程研究

    王海宁:男,1994年生,硕士生,研究方向为模式识别、图像处理

    张天骐:男,1971年生,教授,博士,研究方向为语言信号处理、图像处理、通信信号的调制解调、盲处理、神经网络实现以及FPGA、VLSI实现

    通讯作者:

    乔飞 qiaofei@tsinghua.edu.cn

  • 中图分类号: TN911.73; TP391.4

Measurement Algorithm of Building Vibration Displacement Based on Image Signal Processing

Funds: The National Key R&D Program of China (2017YFC1500601), The National Natural Science Foundation of China (61671095, 61771085, 61702065, 61701067), The Key Research Projects in Teaching Reform of Postgraduate Education in Chongqing City (yjg192019)
  • 摘要: 针对地震后高层建筑物结构损伤监测问题,该文提出一种基于方向码匹配(OCM)和边缘增强匹配(EEM)算法的微小位移测量算法。该算法先将原始图像梯度信息与像素强度融合,增强图像信息;采用相位相关法进行匹配运算,匹配速度比归一化互相关法提升了96.1%;最后使用亚像素插值法,使测量结果达到亚像素精度。实验结果表明,该文算法避免了OCM和EEM算法量化过程中图像梯度信息的损失,大大提高了模板匹配精度,匹配速度比OCM提升了43.3%,比EEM提升了19.6%。
  • 图  1  算法系统框图

    图  2  实验平台

    图  3  黑白格标靶

    图  4  各算法在不同振幅下的测试结果

    图  5  各算法在不同频率下的测试结果

    图  6  各算法在EI Centro地震波上的测试结果

    表  1  位移测量误差对比(1.0 Hz–0.1 mm)

    算法RMSE (mm)NRMSE (%)
    OCM0.156919.1537
    EEM0.081113.7379
    本文算法0.041912.9371
    ORB0.186110.2422
    L_SURB0.045511.5654
    FRIF0.063513.9175
    AKAZE+BRIEF0.048614.0518
    下载: 导出CSV

    表  2  位移测量误差对比(1.0 Hz–0.5 mm)

    算法RMSE (mm)NRMSE (%)
    OCM0.14588.6670
    EEM0.07625.5342
    本文算法0.02082.0162
    ORB0.481312.9148
    L_SURB0.06455.2588
    FRIF0.267815.0890
    AKAZE+BRIEF0.03463.3131
    下载: 导出CSV

    表  3  位移测量误差对比(1.0 Hz–2.0 mm)

    算法RMSE (mm)NRMSE (%)
    OCM0.12992.8043
    EEM0.06941.5857
    本文算法0.02540.6234
    ORB0.50698.4445
    L_SURB0.09132.2266
    FRIF0.11482.7637
    AKAZE+BRIEF0.08161.9882
    下载: 导出CSV

    表  4  位移测量误差对比(1.0 Hz–5.0 mm)

    算法RMSE (mm)NRMSE (%)
    OCM0.17651.7105
    EEM0.13601.3456
    本文算法0.08100.8077
    ORB3.649624.3930
    L_SURB0.33193.2540
    FRIF1.392112.2768
    AKAZE+BRIEF2.903417.7774
    下载: 导出CSV

    表  5  位移测量误差对比(2.0 mm–0.5 Hz)

    算法RMSE (mm)NRMSE (%)
    OCM0.14043.0762
    EEM0.09512.2242
    本文算法0.04071.0115
    ORB0.601611.2924
    L_SURB0.14603.5440
    FRIF0.48379.8989
    AKAZE+BRIEF0.09842.4386
    下载: 导出CSV

    表  6  位移测量误差对比(2.0 mm–1.0 Hz)

    算法RMSE (mm)NRMSE (%)
    OCM0.12992.8043
    EEM0.06941.5857
    本文算法0.02540.6234
    ORB0.50698.4445
    L_SURB0.09132.2266
    FRIF0.11482.7637
    AKAZE+BRIEF0.08161.9882
    下载: 导出CSV

    表  7  位移测量误差对比(2.0 mm–2.0 Hz)

    算法RMSE (mm)NRMSE (%)
    OCM0.14923.4415
    EEM0.09742.2766
    本文算法0.05711.4305
    ORB0.753312.7011
    L_SURB0.11022.7085
    FRIF0.29115.2361
    AKAZE+BRIEF0.37279.2922
    下载: 导出CSV

    表  8  位移测量误差对比(2.0 mm–5.0 Hz)

    算法RMSE (mm)NRMSE (%)
    OCM0.18984.3983
    EEM0.12532.9423
    本文算法0.09832.4761
    ORB0.745814.4986
    L_SURB0.512711.6999
    FRIF0.531311.4219
    AKAZE+BRIEF0.406710.1836
    下载: 导出CSV

    表  9  位移测量误差对比(EI Centro)

    算法RMSE (mm)NRMSE (%)
    OCM0.14882.2487
    EEM0.10721.6262
    本文算法0.07001.0812
    ORB0.811810.5354
    L_SURB0.13792.1263
    FRIF0.24203.7104
    AKAZE+BRIEF0.13202.0248
    下载: 导出CSV

    表  10  帧间运算平均时间对比(EI Centro)

    算法平均运算时间(ms)
    OCM693.5835
    EEM476.2980
    本文算法378.3580
    ORB80.6894
    L-SURB62.1746
    FRIF199.6995
    AKAZE+BRIEF45.2793
    下载: 导出CSV

    表  11  归一化互相关法与相位相关法帧间平均匹配时间对比

    算法平均匹配时间(ms)
    归一化互相关法127.3326
    相位相关法4.9565
    下载: 导出CSV
  • FUKUDA Y, FENG M Q, and SHINOZUKA M. Cost-effective vision-based system for monitoring dynamic response of civil engineering structures[J]. Structural Control and Health Monitoring, 2010, 17(8): 918–936. doi: 10.1002/stc.360
    BREUER P, CHMIELEWSKI T, GÓRSKI P, et al. Application of GPS technology to measurements of displacements of high-rise structures due to weak winds[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2002, 90(3): 223–230. doi: 10.1016/S0167-6105(01)00221-5
    吴元. 一种基于参数更新的机载SAR图像目标定位方法[J]. 电子与信息学报, 2019, 41(5): 1063–1068. doi: 10.11999/JEIT180564

    WU Yuan. An airborne SAR image target location algorithm based on parameter refining[J]. Journal of Electronics &Information Technology, 2019, 41(5): 1063–1068. doi: 10.11999/JEIT180564
    RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB: An efficient alternative to SIFT or SURF[C]. 2011 International Conference on Computer Vision, Barcelona, Spain, 2011: 2564–2571. doi: 10.1109/ICCV.2011.6126544.
    SHU Caiwei and XIAO Xuezhong. ORB-oriented mismatching feature points elimination[C]. 2018 IEEE International Conference on Progress in Informatics and Computing (PIC), Suzhou, China, 2018: 246–249. doi: 10.1109/PIC.2018.8706272.
    WANG Yangping, YONG Jiu, ZHU Zhengping, et al. Augmented reality tracking registration based on improved KCF tracking and ORB feature detection[C]. The 7th International Conference on Information, Communication and Networks (ICICN), Macao, China, 2019: 230–233. doi: 10.1109/ICICN.2019.8834947.
    WANG Zhenhua, FAN Bin, and WU Fuchao. FRIF: Fast robust invariant feature[C]. British Machine Vision Conference 2013, Bristol, UK, 2013. doi: 10.5244/C.27.16.
    WANG Xiangyang, WANG Chao, WANG Li, et al. A fast and high accurate image copy-move forgery detection approach[J]. Multidimensional Systems and Signal Processing, 2020, 31(3): 857–883. doi: 10.1007/s11045-019-00688-x
    WANG Xu, ZOU Jiabao, and SHI Daosheng. An improved ORB image feature matching algorithm based on SURF[C]. The 3rd International Conference on Robotics and Automation Engineering (ICRAE), Guangzhou, China, 2018: 218-222. doi: 10.1109/ICRAE.2018.8586755.
    WANG Xinzhu, LV Xuliang, LI Ling, et al. A new method of speeded up robust features image registration based on image preprocessing[C]. 2018 International Conference on Information Systems and Computer Aided Education (ICISCAE), Changchun, China, 2018: 317-321. doi: 10.1109/ICISCAE.2018.8666894.
    牛燕雄, 陈梦琪, 张贺. 基于尺度不变特征变换的快速景象匹配方法[J]. 电子与信息学报, 2019, 41(3): 626–631. doi: 10.11999/JEIT180440

    NIU Yanxiong, CHEN Mengqi, and ZHANG He. Fast scene matching method based on scale invariant feature transform[J]. Journal of Electronics and Information Technology, 2019, 41(3): 626–631. doi: 10.11999/JEIT180440
    TAREEN S A K and SALEEM Z. A comparative analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK[C]. 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, Pakistan, 2018: 1–10. doi: 10.1109/ICOMET.2018.8346440.
    黄建坤. 基于图像序列的桥梁形变位移测量方法[D].[硕士论文], 西南交通大学, 2018.

    HUANG Jiankun. Displacement measurement method for bridge deformation based on image sequence[D].[Master dissertation], Southwest Jiaotong University, 2018.
    FUKUDA Y, FENG M Q, NARITA Y, et al. Vision-based displacement sensor for monitoring dynamic response using robust object search algorithm[J]. IEEE Sensors Journal, 2013, 13(12): 4725–4732. doi: 10.1109/JSEN.2013.2273309
    LUO Longxi and FENG M Q. Edge‐enhanced matching for gradient-based computer vision displacement measurement[J]. Computer-Aided Civil and Infrastructure Engineering, 2018, 33(12): 1019–1040. doi: 10.1111/mice.12415
    刘有桥. 基于图像处理的轨道位移监测系统研究[J]. 计算机应用与软件, 2019, 36(1): 246–250, 315. doi: 10.3969/j.issn.1000-386x.2019.01.044

    LIU Youqiao. Track displacement monitoring system based on image processing[J]. Computer Applications and Software, 2019, 36(1): 246–250, 315. doi: 10.3969/j.issn.1000-386x.2019.01.044
    LUO Longxi, FENG M Q, and WU Z Y. Robust vision sensor for multi-point displacement monitoring of bridges in the field[J]. Engineering Structures, 2018, 163: 255–266. doi: 10.1016/j.engstruct.2018.02.014
  • 加载中
图(6) / 表(11)
计量
  • 文章访问数:  2585
  • HTML全文浏览量:  692
  • PDF下载量:  52
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-10-16
  • 修回日期:  2020-04-12
  • 网络出版日期:  2020-04-28
  • 刊出日期:  2020-10-13

目录

    /

    返回文章
    返回

    官方微信,欢迎关注