Automatic Displacement and Vibration Measurement in Laboratory Experiments with A Deep Learning Method

TL;DR

This paper introduces a deep learning pipeline using Mask R-CNN combined with signal processing techniques to automatically and accurately measure displacement and vibration in laboratory structural tests.

Contribution

It presents a novel integration of Mask R-CNN with SIFT and filtering methods for precise motion tracking in structural experiments.

Findings

Accurate automatic measurement of structural displacement and vibration achieved

Method verified on reinforced concrete beams and shaking table tests

Deep learning approach outperforms traditional manual measurement techniques

Abstract

This paper proposes a pipeline to automatically track and measure displacement and vibration of structural specimens during laboratory experiments. The latest Mask Regional Convolutional Neural Network (Mask R-CNN) can locate the targets and monitor their movement from videos recorded by a stationary camera. To improve precision and remove the noise, techniques such as Scale-invariant Feature Transform (SIFT) and various filters for signal processing are included. Experiments on three small-scale reinforced concrete beams and a shaking table test are utilized to verify the proposed method. Results show that the proposed deep learning method can achieve the goal to automatically and precisely measure the motion of tested structural members during laboratory experiments.