|
|
Research on Structural Displacement Tracking Monitoring Based on Surveillance Camera Videos |
GAO Chao1,WANG Liu-qi1,NI Tong-yuan1,2,YANG Yang1,2,LIU Jin-tao1,2,GU Chun-ping1,2 |
1. College of Civil Engineering,Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China
2. Key Laboratory of Civil Engineering Structures & Disaster Prevention and Mitigation Technology of Zhejiang Province, Hangzhou, Zhejiang 310023, China |
|
|
Abstract Based on the video of surveillance camera and digital image processing, a method to characterize the displacement of structural position feature points was designed. Firstly, the circular target is placed on the structure. The template matching method is used to detect the initial image coordinate region of the circular target, and the region of interest(ROI) is cut out; Then use Otsu method to determine the binarization threshold of the region, extract the coordinates of the target contour points, use the least square method to fit the contour into an ellipse, and obtain the coordinates of the center of the ellipse and the size of the long and short axis. The ratio of the diameter of the circular target to the major and minor axis is the conversion coefficient η; The product of the change of the ellipse center coordinate and the conversion coefficient is the displacement of the structure in the actual corresponding coordinate direction. The experimental results shown that the relative error of this method was less than 3% in the horizontal and vertical displacement loading experiments which compared with the high-precision displacement meter. And it can realize millimeter displacement monitoring and accurately characterize the displacement change of the corresponding characteristic points of the structure.
|
Received: 12 July 2022
Published: 18 April 2023
|
|
Fund:National Natural Science Foundation of China;Zhejiang Province Science and Technology Plan Project - Provincial Key Research and Development Plan;Zhejiang Provincial Education Department Scientific research project |
|
|
|
[19] |
丁频一, 刘铭豪, 柳春艳, 等. 我国道路交通技术监控设备质量检测分析研究[C]//第十五届中国智能交通年会论文集 , 2020:223-231.
|
[6] |
方江静, 陈巨兵, 孙晨. 基于DIC的渐开线直齿轮接触变形实验研究 [J]. 实验力学, 2021, 36 (5): 571-580.
|
[4] |
杨仁树, 李炜煜, 李永亮, 等. 3种岩石动态拉伸力学性能试验与对比分析 [J]. 煤炭学报, 2020, 45 (9): 3107-3118.
|
[11] |
王翔, 王鹏, 程辉. 基于机器视觉的桥梁形变在线监测技术研究 [J]. 公路工程, 2014, 39 (1): 198-201.
|
[25] |
毕振波, 张世友, 杨花, 等. 基于浅层机器学习的视频监控船舶检测综述 [J]. 系统仿真学报, 2021, 33 (12): 2792-2807.
|
[1] |
孙利民,尚志强, 夏烨. 大数据背景下的桥梁结构健康监测研究现状与展望 [J]. 中国公路学报, 2019, 32 (11): 1-20.
|
[3] |
Pan B, Chen B. A novel mirror-assisted multi-view digital image correlation for dual-surface shape and deformation measurements of sheet samples [J]. Optics and Lasers in Engineering, 2019, 121 (10): 512-520.
|
[7] |
Gao Y, Cheng T, Su Y, et al. High-efficiency and high-accuracy digital image correlation for three-dimensional measurement [J]. Optics and Lasers in Engineering, 2015, 65 (2): 73-80.
|
[15] |
倪彤元, 周若虚, 杨杨, 等. 基于智能手机APP的图像法检测混凝土表面裂缝研究 [J]. 计量学报, 2021, 42 (2): 163-170.
|
|
Chen J Q, Jin X H, Wang W Y, et al. Vehicle Flow Detection Based on YOLOv3 and DeepSort [J]. Acta Metrologica Sinica, 2021, 42 (6): 718-723。
|
[22] |
Zhang Z Y. A flexible new technique for camera calibration [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22 (11): 1330-1334.
|
[2] |
伊廷华, 王浩, 丁幼亮. 大跨桥梁持续环境荷载的时变效应与服役性能评估 [J]. 中国基础科学, 2019, 21 (6): 44-48.
|
[24] |
袁小翠, 吴禄慎, 陈华伟. 基于Otsu方法的钢轨图像分割 [J]. 光学精密工程, 2016, 24 (7): 1772-1781.
|
|
Sun L M, Shang Z Q, Xia Y. Development and Prospect of Bridge Structural Health Monitoring in the Context of Big Data [J]. China Journal of Highway and Transport, 2019, 32 (11): 1-20.
|
|
Yi Y H, Wang H, Ding Y L. Time-Varying Effects and Service Performance Evaluation for Long-Span Bridges Under the Effects of Continuous Environmental Loads[J]. China Basic Science, 2019, 21 (6): 44-48.
|
[5] |
Kim J, Janabi-Sharifi F. A Haptic Interaction Method Using Visual Information and Physically Based Modeling [J]. IEEE/ASME Transactions on Mechatronics, 2010, 15 (4): 636-645.
|
|
Fang J J, Chen J B, Sun C. Experimental study on contact deformation of involute spur gear based on DIC [J]. Journal of Experimental Mechanics, 2021, 36 (5): 571-580.
|
[17] |
陈佳倩, 金晅宏, 王文远, 等. 基于YOLOv3和DeepSort的车流量检测 [J]. 计量学报, 2021, 42 (6): 718-723.
|
|
Yang R S, Li W Y, Li Y L, et al. Comparative analysis on dynamic tensile mechanical properties of three kinds of rocks [J]. Journal of China Coal Society, 2020, 45 (9): 3107-3118.
|
[8] |
Ma S, Pang J, Ma Q. The systematic error in digital image correlation induced by self-heating of a digital camera [J]. Measurement Science & Technology, 2012, 23 (2): 025403.
|
[9] |
Pan B, Tian L, Song X. Real-time, non-contact and targetless measurement of vertical deflection of bridges using off-axis digital image correlation [J]. NDT & E International, 2016, 79 (4): 73-80.
|
[10] |
Lee J J, Shinozuka M. Real-Time Displacement Measurement of a Flexible Bridge Using Digital Image Processing Techniques [J]. Experimental Mechanics, 2006, 46 (1): 105-114.
|
|
Wang X, Wang P and Cheng H. Research of Bridge Displacement On-line Monitoring Technique Based on Machine Vision [J]. Highway Engineering, 2014, 39 (1): 198-201.
|
[12] |
叶肖伟, 董传智. 基于计算机视觉的结构位移监测综述 [J]. 中国公路学报, 2019, 32 (11): 21-39.
|
[13] |
Zhou Z, Shao S, Deng G, et al. Vision-based modal parameter identification for bridges using a novel holographic visual sensor [J]. Measurement, 2021, 179 (7): 109551.
|
|
Ni T Y, Zhou R X, Yang Y, et al. 2021. Research on Detection of Concrete Surface Cracks by Image Processing Based on Smartphone APP [J]. Acta Metrologica Sinica, 42 (2): 163-170.
|
[16] |
Tian L, Zhang X, Pan B. Cost-Effective and Ultraportable Smartphone-Based Vision System for Structural Deflection Monitoring [J]. Journal of Sensors, 2021, 2021 (2): 8843857.
|
|
Guo Q, Zhou S, He X, Shao H P, et al. Construction of Virtual Channel and Ship Yaw Detection Method in Bridge Collision Avoidance System [J]. Software Guide, 2018, 17 (7): 189-192.
|
[20] |
Zare H A, Tehrani M H, Harvey P S. Modal identification of building structures using vision-based measurements from multiple interior surveillance cameras [J]. Engineering Structures, 2021, 228 (1): 111517.
|
[26] |
涂伟, 李清泉, 高文武, 等. 基于机器视觉的桥梁挠度实时精密测量方法 [J]. 测绘地理信息, 2020, 45 (6): 80-87.
|
|
Ye X W and Dong C Z. Review of Computer Vision-based Structural Displacement Monitoring [J]. China Journal of Highway and Transport, 2019, 32 (11): 21-39.
|
[14] |
Ni T, Zhou R, Gu C, et al. Measurement of concrete crack feature with android smartphone APP based on digital image processing techniques [J]. Measurement, 2019, 150 (1): 107093.
|
[18] |
郭乾, 周曙, 何信, 等. 一种桥梁防撞系统中的虚拟航道构建与船舶偏航检测方法 [J]. 软件导刊, 2018, 17 (7): 189-192.
|
[21] |
Xu Y, Brownjohn J, Kong D. A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge [J]. Structural Control and Health Monitoring, 2018, 25 (5): e2155.
|
|
Bi Z B, Zhang S Y, Yang H, et al. Survey of Ship Detection in Video Surveillance Based on Shallow Machine Learning [J]. Journal of System Simulation, 2021, 33 (12): 2792-2807.
|
|
Tu W, Li Q Q, Gao W W, et al. Monitoring the Dynamic Deflection of Bridges Using Computer Vision [J]. Journal of Geomatics, 2020, 45 (6): 80-87.
|
|
Ye X W, Zhang X M, Ni Y Q, et al. Bridge deflection measurement method based on machine vision technology [J]. Journal of Zhejiang University (Engineerin Science), 2014, 48 (5): 813-819.
|
[23] |
Otsu N. A Threshold Selection Method from Gray-Level Histograms [J]. IEEE Transactions on Systems Man & Cybernetics, 2007, 9 (1): 62-66.
|
|
Yuan X C, Wu L S, Chen H W. Rail image segmentation based on Otsu threshold method [J]. Optics and Precision Engineering, 2016, 24 (7): 1772-1781.
|
[27] |
叶肖伟, 张小明, 倪一清, 等. 基于机器视觉技术的桥梁挠度测试方法 [J]. 浙江大学学报 (工学版), 2014, 48 (5): 813-819.
|
|
|
|