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

arXiv: https://arxiv.org/abs/2109.04960 · HTML: https://mlsanity.com/paper/2109.04960/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 10 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [High-Resolution Representations for Labeling Pixels and Regions](https://mlsanity.com/paper/1904.04514/) (2019)
- [End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales](https://mlsanity.com/paper/2011.03098/) (2020)

## Abstract (arXiv)

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.