A Robust Vision-Based Method for Displacement Measurement under Adverse Environmental Factors Using Spatio-Temporal Context Learning and Taylor Approximation
A Robust Vision-Based Method for Displacement Measurement under Adverse Environmental Factors Using Spatio-Temporal Context Learning and Taylor Approximation
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DOI：
10.3390/s19143197
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发表时间：
2019-07-12
期刊：
影响因子：
3.9
通讯作者：
Taylor, Su
中科院分区：
文献类型：
--
作者：
Dong, Chuan-Zhi;Celik, Ozan;Taylor, Su
目前，大多数基于视觉的测量研究都是在理想的环境下进行的，以确保足够的测量性能和准确性。然而，基于视觉的系统可能会面临一些不利的影响因素，如光照变化和雾干扰，这可能会影响测量精度。本文提出了一种鲁棒的基于视觉的位移测量方法，可以处理上述两个常见的和重要的不利因素，并实现亚像素级的灵敏度。所提出的方法利用了高分辨率成像的优势，将空间和时间的上下文方面。为了验证该方法的可行性、稳定性和鲁棒性，在一座两跨三车道桥梁上进行了一系列的实验。在实验室中模拟了光照变化和雾的干扰。所提出的方法的结果进行了比较，传统的位移传感器的数据和当前的基于视觉的方法的结果。实验结果表明，在光照变化和雾天干扰下，该方法的测量结果优于现有方法。
Currently, the majority of studies on vision-based measurement have been conducted under ideal environments so that an adequate measurement performance and accuracy is ensured. However, vision-based systems may face some adverse influencing factors such as illumination change and fog interference, which can affect measurement accuracy. This paper developed a robust vision-based displacement measurement method which can handle the two common and important adverse factors given above and achieve sensitivity at the subpixel level. The proposed method leverages the advantage of high-resolution imaging incorporating spatial and temporal contextual aspects. To validate the feasibility, stability, and robustness of the proposed method, a series of experiments was conducted on a two-span three-lane bridge in the laboratory. The illumination changes and fog interference were simulated experimentally in the laboratory. The results of the proposed method were compared to conventional displacement sensor data and current vision-based method results. It was demonstrated that the proposed method gave better measurement results than the current ones under illumination change and fog interference.