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基于计算机视觉的在用桥梁结构位移测量,对光致图像退化具有鲁棒性

Computer Vision-Based Structural Displacement Measurement Robust to Light-Induced Image Degradation for In-Service Bridges.

作者信息

Lee Junhwa, Lee Kyoung-Chan, Cho Soojin, Sim Sung-Han

机构信息

School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea.

Korea Railroad Research Institute, Uiwang 16105, Korea.

出版信息

Sensors (Basel). 2017 Oct 11;17(10):2317. doi: 10.3390/s17102317.

Abstract

The displacement responses of a civil engineering structure can provide important information regarding structural behaviors that help in assessing safety and serviceability. A displacement measurement using conventional devices, such as the linear variable differential transformer (LVDT), is challenging owing to issues related to inconvenient sensor installation that often requires additional temporary structures. A promising alternative is offered by computer vision, which typically provides a low-cost and non-contact displacement measurement that converts the movement of an object, mostly an attached marker, in the captured images into structural displacement. However, there is limited research on addressing light-induced measurement error caused by the inevitable sunlight in field-testing conditions. This study presents a computer vision-based displacement measurement approach tailored to a field-testing environment with enhanced robustness to strong sunlight. An image-processing algorithm with an adaptive region-of-interest (ROI) is proposed to reliably determine a marker's location even when the marker is indistinct due to unfavorable light. The performance of the proposed system is experimentally validated in both laboratory-scale and field experiments.

摘要

土木工程结构的位移响应能够提供有关结构行为的重要信息,有助于评估结构的安全性和适用性。使用传统设备(如线性可变差动变压器(LVDT))进行位移测量具有挑战性,因为传感器安装不便,通常需要额外的临时结构。计算机视觉提供了一种很有前景的替代方法,它通常能提供低成本的非接触式位移测量,即将捕获图像中物体(主要是附着的标记)的移动转换为结构位移。然而,针对现场测试条件下不可避免的阳光导致的光致测量误差,相关研究较少。本研究提出了一种基于计算机视觉的位移测量方法,该方法适用于现场测试环境,对强光具有更强的鲁棒性。提出了一种具有自适应感兴趣区域(ROI)的图像处理算法,即使在光线不利导致标记不清晰的情况下,也能可靠地确定标记的位置。所提系统的性能在实验室规模实验和现场实验中均得到了实验验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/03c8/5677402/b1ef7787ce0b/sensors-17-02317-g001.jpg

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