Modeling and detection of heat haze in computer vision based displacement measurement

被引：17
作者：
Luo, Longxi [1 ,2 ]
Feng, Maria Q. [3 ]
Wu, Jianping [2 ]
Bi, Luzheng [1 ]
机构：
[1] Beijing Inst Technol, Sch Mech Engn, Beijing, Peoples R China
[2] Tsinghua Univ, Inst Transportat Engn, Beijing, Peoples R China
[3] Columbia Univ, Sensing Monitoring & Robot Lab, New York, NY 10027 USA
来源：
MEASUREMENT | 2021年 / 182卷
基金：
中国国家自然科学基金;
关键词：
Computer vision; Heat haze error model; Image distortion estimation; Heat haze detection; Structural displacement measurement; ATMOSPHERIC-TURBULENCE; SYSTEM; SENSOR;
D O I：
10.1016/j.measurement.2021.109772
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Computer vision has become widely applied for structural displacement monitoring. However, heat haze is one of the major challenges. Image distortions caused by heat haze in hot weather can result in displacement errors. Therefore, a comprehensive study of properties of heat haze-induced distortions and displacement errors is conducted. Firstly, an image distortion estimation method is proposed for estimating heat haze-induced image distortions. Secondly, displacement errors due to heat haze are analyzed. A heat haze error model is formulated to describe the properties of heat haze errors, and the explicit effect of the environmental factor of temperature on the heat haze error model. Thirdly, a heat haze detection method is proposed to enable detection of heat haze's influence on vision-based displacement sensors by extracting features from distortion measurements and applying a classification algorithm. Field tests in hot weather and experiments with dark heaters for introducing heat haze are conducted for validations.
引用
收藏
页数：12
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共 49 条
[1]   3D dynamic displacement-field measurement for structural health monitoring using inexpensive RGB-D based sensor [J].
Abdelbarr, Mohamed ;
Chen, Yulu Luke ;
Jahanshahi, Mohammad R. ;
Masri, Sami F. ;
Shen, Wei-Men ;
Qidwai, Uvais A. .
SMART MATERIALS AND STRUCTURES, 2017, 26 (12)
[2]   A vision-based system for measuring the displacements of large structures: Simultaneous adaptive calibration and full motion estimation [J].
Almeida Santos, C. ;
Oliveira Costa, C. ;
Batista, J. .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2016, 72-73 :678-694
[3]   Atmospheric Turbulence Mitigation Using Complex Wavelet-Based Fusion [J].
Anantrasirichai, Nantheera ;
Achim, Alin ;
Kingsbury, Nick G. ;
Bull, David R. .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2013, 22 (06) :2398-2408
[4]   A hybrid artificial neural network-GJR modeling approach to forecasting currency exchange rate volatility [J].
Baffour, Alexander Amo ;
Feng, Jingchun ;
Taylorb, Evans Kwesi .
NEUROCOMPUTING, 2019, 365 :285-301
[5]   Automatic robust estimation for exponential smoothing: Perspectives from statistics and machine learning [J].
Barrow, Devon ;
Kourentzes, Nikolaos ;
Sandberg, Rickard ;
Niklewski, Jacek .
EXPERT SYSTEMS WITH APPLICATIONS, 2020, 160
[6]   Target-less computer vision for traffic signal structure vibration studies [J].
Bartilson, Daniel T. ;
Wieghaus, Kyle T. ;
Hurlebaus, Stefan .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2015, 60-61 :571-582
[7]   Fully integrated carbon nanotube composite thin film strain sensors on flexible substrates for structural health monitoring [J].
Burton, A. R. ;
Lynch, J. P. ;
Kurata, M. ;
Law, K. H. .
SMART MATERIALS AND STRUCTURES, 2017, 26 (09)
[8]   Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition [J].
Buyuksahin, Umit Cavus ;
Ertekina, Seyda .
NEUROCOMPUTING, 2019, 361 :151-163
[9]   Modeling of the Dynamics of Non-radiative Energy Transfer in Tm3+, Tb3+: LiYF4-Based Electronic Materials [J].
Castaneda-Miranda, A. ;
Castano, V. M. .
JOURNAL OF ELECTRONIC MATERIALS, 2017, 46 (08) :5107-5111
[10]   Internet of things for smart farming and frost intelligent control in greenhouses [J].
Castaneda-Miranda, Alejandro ;
Castano-Meneses, Victor M. .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2020, 176
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