Preprint Article Version 1 This version is not peer-reviewed

A Robust Vision-based Method for Displacement Measurement under Adverse Environmental Factors using Spatio-Temporal Context Learning and Taylor Approximation

Version 1 : Received: 31 May 2019 / Approved: 3 June 2019 / Online: 3 June 2019 (12:59:00 CEST)

A peer-reviewed article of this Preprint also exists.

Dong, C.-Z.; Celik, O.; Catbas, F.N.; OBrien, E.; Taylor, S. A Robust Vision-Based Method for Displacement Measurement under Adverse Environmental Factors Using Spatio-Temporal Context Learning and Taylor Approximation. Sensors 2019, 19, 3197. Dong, C.-Z.; Celik, O.; Catbas, F.N.; OBrien, E.; Taylor, S. A Robust Vision-Based Method for Displacement Measurement under Adverse Environmental Factors Using Spatio-Temporal Context Learning and Taylor Approximation. Sensors 2019, 19, 3197.

Journal reference: Sensors 2019, 19, 3197
DOI: 10.3390/s19143197

Abstract

Currently the majority of studies on vision-based measurement has 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 the measurement accuracy. This paper develops 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 context 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 change and fog interference are simulated experimentally in the laboratory. The results of the proposed method are compared to conventional displacement sensor data and current vision-based method results. It is demonstrated that the proposed method gives better measurement results than the current ones under illumination change and fog interference.

Subject Areas

structural health monitoring; displacement measurement; non-contact; computer vision, environmental factors; spatio-temporal context; Taylor approximatio

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