A robust subpixel refinement technique using self‐adaptive edge points matching for vision‐based structural displacement measurement. (20th July 2022)

• Record Type: Journal Article
• Authors: Wang, Miaomin; Xu, Fuyou; Xu, Yan; Brownjohn, James
• Is Part Of: Computer-aided civil and infrastructure engineering. Volume 38:Number 5(2023)
• Page Start: 562
• Page End: 579
• Publication Date: 2022-07-20
• Subjects: Civil engineering -- Data processing -- Periodicals; Computer-aided engineering -- Periodicals; 624.0285
• DOI: 10.1111/mice.12889
• Languages: English
• ISSNs: 1093-9687

Abstract

Abstract: Applying a subpixel refinement technique in vision‐based displacement sensing can significantly improve the measurement accuracy. However, digital image signals from the camera are highly sensitive to drastically varying lighting conditions in the field measurements of structural displacement, causing pixels expressing a tracking target to have nonuniform grayscale intensity changes in different recording video frames. Traditional feature points‐based subpixel refinement techniques are neither robust nor accurate enough in this case, presenting challenges for accurate measurement. This paper proposes a robust subpixel refinement technique—self‐adaptive edge points matching (SEPM)—to obtain accurate subpixel‐level displacements under drastic illumination change conditions. Different from traditional feature points‐based methods, the gradient and shape information of the target edge contour are used in the SEPM calculation. Three tests under different illumination conditions were conducted to evaluate the performance of the SEPM. The results show that the SEPM is capable of producing accurate subpixel‐level displacement data with less 1/16‐pixel root mean‐square error.