Holistically Nested Edge Detection and particle filtering for subtle vibration extraction

Abstract

Extracting and analyzing subtle motion via optical sensing techniques is becoming a more important research area within the vibration community. Computer vision approaches and machine learning architecture have been adopted to contest the complexity concerning hand instrumentation of sensors. Pre-treatment using a stochastic pattern for optical analysis or traditional wired sensing is not always permitted for large structures. In this work, Holistically Nested Edge Detection (HED) is utilized to highlight high-spatial-frequency content features in images for structural dynamic analysis. Following the accentuation of edge features, phase-based motion magnification (PMM) is used to amplify motion that is not visible to the naked eye. This permits the band-passing of higher order resonant frequencies for structural dynamic evaluation. Implementation of two-dimensional particle filtering (PF) is utilized to track the accentuated edge features in the presence of ghosting artifacts that appear post magnification. Within this work, compared to traditional edge-thresholding, the combination of HED and PF (HED-PF) is more proficient to handle noise and ultimately improves magnified image quality and modal parameter extraction. Uncertainties related to using PF for vibration measurement are analytically presented to provide guidance on the behavior of the algorithm. To demonstrate the effectiveness of HED-PF, experimental modal testing takes place on both a tier-structure and cantilever beam to demonstrate the approaches’ capability in handling varying sorts of excitation. Finally, introduction of background occlusions and non-ideal lighting scenarios are used to test the limitations of the approach. Ultimately, this will highlight HED-PF’s robust tracking capabilities compared to the current state of the art.