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基于圖像處理的建筑物振動位移測量算法

陳昌川,  李奎,  喬飛,  姜宏偉,  趙曼淇,  公茂盛,  王海寧,  張?zhí)祢U

陳昌川, 李奎, 喬飛, 姜宏偉, 趙曼淇, 公茂盛, 王海寧, 張?zhí)祢U. 基于圖像處理的建筑物振動位移測量算法[J]. 電子與信息學(xué)報, 2020, 42(10): 2516-2523. doi: 10.11999/JEIT190805
引用本文: 陳昌川, 李奎, 喬飛, 姜宏偉, 趙曼淇, 公茂盛, 王海寧, 張?zhí)祢U. 基于圖像處理的建筑物振動位移測量算法[J]. 電子與信息學(xué)報, 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 & 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 & Information Technology, 2020, 42(10): 2516-2523. doi: 10.11999/JEIT190805

基于圖像處理的建筑物振動位移測量算法

doi: 10.11999/JEIT190805
  • 1.

    重慶郵電大學(xué)通信與信息工程學(xué)院 重慶 400065

  • 2.

    清華大學(xué)電子工程系 北京 100084

  • 3.

    重慶郵電大學(xué)信號與信息處理重慶市重點實驗室 重慶 400065

  • 4.

    中國地震局工程力學(xué)研究所中國地震局地震工程與工程振動重點實驗室 哈爾濱 150080

基金項目: 國家重點研發(fā)計劃(2017YFC1500601)，國家自然科學(xué)基金(61671095, 61771085, 61702065, 61701067)；重慶市研究生教育教學(xué)改革研究重點項目(yjg192019)
詳細(xì)信息
    作者簡介:

    陳昌川：男，1978年生，副教授，研究方向為智能信息處理、圖像處理、移動通信

    李奎：男，1990年生，碩士生，研究方向為圖像與信號處理、目標(biāo)檢測與識別

    喬飛：男，1977年生，副研究員，博士，研究方向為集成智能、信號處理

    姜宏偉：男，1996年生，碩士生，研究方向為圖像處理

    趙曼淇：男，1997年生，博士生，研究方向為無人機(jī)目標(biāo)檢測追蹤

    公茂盛：男，1976年生，研究員，博士，研究方向為地震工程研究

    王海寧：男，1994年生，碩士生，研究方向為模式識別、圖像處理

    張?zhí)祢U：男，1971年生，教授，博士，研究方向為語言信號處理、圖像處理、通信信號的調(diào)制解調(diào)、盲處理、神經(jīng)網(wǎng)絡(luò)實現(xiàn)以及FPGA、VLSI實現(xiàn)

    通訊作者:

    喬飛　qiaofei@tsinghua.edu.cn

  • 中圖分類號: TN911.73; TP391.4

Measurement Algorithm of Building Vibration Displacement Based on Image Signal Processing

  • 1.

    School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

  • 2.

    Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

  • 3.

    Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

  • 4.

    Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China

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: 針對地震后高層建筑物結(jié)構(gòu)損傷監(jiān)測問題，該文提出一種基于方向碼匹配(OCM)和邊緣增強(qiáng)匹配(EEM)算法的微小位移測量算法。該算法先將原始圖像梯度信息與像素強(qiáng)度融合，增強(qiáng)圖像信息；采用相位相關(guān)法進(jìn)行匹配運(yùn)算，匹配速度比歸一化互相關(guān)法提升了96.1%；最后使用亞像素插值法，使測量結(jié)果達(dá)到亞像素精度。實驗結(jié)果表明，該文算法避免了OCM和EEM算法量化過程中圖像梯度信息的損失，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%。
    Abstract: A micro-displacement measurement algorithm is proposed based on the Orientation Code Matching (OCM) and Edge Enhanced Matching (EEM) algorithms for monitoring the structural damage of tall buildings after earthquake. The algorithm first fuses the gradient information of the original image with the pixel intensity to enhance the image information; Then the phase correlation method is used to perform the matching operation, the matching speed is 96.1% higher than the normalized cross-correlation method; Finally, the sub-pixel interpolation method is used to make the measurement achieve sub-pixel accuracy. Experimental results show that the proposed algorithm avoids the loss of image gradient information during the quantization of OCM and EEM algorithms, greatly improves the template matching accuracy, and the matching speed is 43.3% higher than OCM and 19.6% higher than EEM.
    • HTML全文

  • 圖  1  算法系統(tǒng)框圖

    圖  2  實驗平臺

    圖  3  黑白格標(biāo)靶

    圖  4  各算法在不同振幅下的測試結(jié)果

    圖  5  各算法在不同頻率下的測試結(jié)果

    圖  6  各算法在EI Centro地震波上的測試結(jié)果

    表  1  位移測量誤差對比(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
    下載: 導(dǎo)出CSV

    表  2  位移測量誤差對比(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
    下載: 導(dǎo)出CSV

    表  3  位移測量誤差對比(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
    下載: 導(dǎo)出CSV

    表  4  位移測量誤差對比(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
    下載: 導(dǎo)出CSV

    表  5  位移測量誤差對比(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
    下載: 導(dǎo)出CSV

    表  6  位移測量誤差對比(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
    下載: 導(dǎo)出CSV

    表  7  位移測量誤差對比(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
    下載: 導(dǎo)出CSV

    表  8  位移測量誤差對比(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
    下載: 導(dǎo)出CSV

    表  9  位移測量誤差對比(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
    下載: 導(dǎo)出CSV

    表  10  幀間運(yùn)算平均時間對比(EI Centro)

    算法平均運(yùn)算時間(ms)
    OCM693.5835
    EEM476.2980
    本文算法378.3580
    ORB80.6894
    L-SURB62.1746
    FRIF199.6995
    AKAZE+BRIEF45.2793
    下載: 導(dǎo)出CSV

    表  11  歸一化互相關(guān)法與相位相關(guān)法幀間平均匹配時間對比

    算法平均匹配時間(ms)
    歸一化互相關(guān)法127.3326
    相位相關(guān)法4.9565
    下載: 導(dǎo)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
    吳元. 一種基于參數(shù)更新的機(jī)載SAR圖像目標(biāo)定位方法[J]. 電子與信息學(xué)報, 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, 陳夢琪, 張賀. 基于尺度不變特征變換的快速景象匹配方法[J]. 電子與信息學(xué)報, 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.
    黃建坤. 基于圖像序列的橋梁形變位移測量方法[D].[碩士論文], 西南交通大學(xué), 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
    劉有橋. 基于圖像處理的軌道位移監(jiān)測系統(tǒng)研究[J]. 計算機(jī)應(yīng)用與軟件, 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
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  • 收稿日期:  2019-10-16
  • 修回日期:  2020-04-12
  • 網(wǎng)絡(luò)出版日期:  2020-04-28
  • 刊出日期:  2020-10-13

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