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.

计算机视觉已广泛应用于结构位移监测。然而，热雾是主要挑战之一。炎热天气中由热雾引起的图像失真会导致位移误差。因此，对热雾引起的畸变和位移误差的特性进行了综合研究。首先，提出了一种用于估计热雾引起的图像失真的图像失真估计方法。其次，分析了热雾引起的位移误差。建立了热雾误差模型来描述热雾误差的特性，以及温度环境因素对热雾误差模型的显式影响。第三，提出了一种热雾检测方法，通过从失真测量中提取特征并应用分类算法，能够检测热雾对基于视觉的位移传感器的影响。进行了炎热天气的现场测试和使用暗加热器引入热雾的实验以进行验证。