A robust subpixel refinement technique using self-adaptive edge points matching for vision-based structural displacement measurement

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.

Automated summary

This paper proposes a subpixel refinement technique called self-adaptive edge points matching (SEPM) to accurately measure subpixel-level displacements under varying lighting conditions. The SEPM utilizes the gradient and shape information of the target edge contour and has demonstrated high accuracy in three different illumination tests.