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Sensors 2017, 17(10), 2317; https://doi.org/10.3390/s17102317

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

1
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
2
Korea Railroad Research Institute, Uiwang 16105, Korea
3
Department of Civil and Environmental Engineering, University of Seoul, Seoul 02504, Korea
*
Authors to whom correspondence should be addressed.
Received: 2 July 2017 / Revised: 3 October 2017 / Accepted: 9 October 2017 / Published: 11 October 2017
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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. View Full-Text
Keywords: adaptive ROI; computer vision; displacement adaptive ROI; computer vision; displacement
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Lee, J.; Lee, K.-C.; Cho, S.; Sim, S.-H. Computer Vision-Based Structural Displacement Measurement Robust to Light-Induced Image Degradation for In-Service Bridges. Sensors 2017, 17, 2317.

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