Video Motion Magnification and Subpixel Edge Detection-Based Full-Field Dynamic Displacement Measurement

Abstract

Noncontact measurement techniques in structural dynamics field have progressed significantly in the past few decades. Vision-based measurement techniques are unique in that they have the ability to achieve full-field measurement and possess the typical advantages associated with noncontact measurement techniques. Recently, vision-based techniques have also been applied to streaming of videos for structural dynamic displacement measurement. The most recent trends in vision-based measurements include target tracing, digital image correlation, and target-less approaches. There are, however, some shortcomings of the vision-based techniques such as susceptibilities to image noise, prevailing light conditions, and limit in measurement resolution. To reduce these shortcomings, a method known as video motion magnification (MM) can be used to amplify small structural motions. Using the phase-based motion magnification (PBMM) and subpixel edge detection methods, the full-field dynamic displacements of the structure can be obtained. The deep convolutional long short-term memory (ConvLSTM) network is applied to aid in the selection of the frequency band for magnification in the PBMM algorithm. To achieve higher measurement accuracy, the displacement results with and without MM are combined with the finite impulse response (FIR) filter which can reduce the error caused by the PBMM procedure. In the tests, plastic optical fiber (POF) displacement sensors are introduced and used as reference measurements to compare the dynamic displacement results from the proposed vision-based method. Compared with the measured displacements with POF sensors, the proposed method offers high level of accuracy for full-field displacement measurement.