Vibration measurement in machine vision based on ROI gray-scale projection feature matching algorithm

Abstract

The current machine vision-based vibration measurement faces numerous challenges, such as low sampling frequency, excessive computational time, and expensive high-definition, high-frame-rate industrial cameras. In response to these issues, this study proposed a feature-matching algorithm that combines ROI image interpolation with gray-scale projection. This algorithm allows cost-effective industrial cameras with lower resolution but higher frame rates. The system effectively identifies modal parameters by executing interpolation and gray-scale projection processing on the ROI images and matching the generated gray-scale projection features. This allows for precisely capturing dynamic positional changes at measurement points. To validate the effectiveness of the proposed method, this study performed impact tests on a laboratory-based model of a beam that was simply supported. The experiments aimed to simulate adverse real-world conditions such as non-uniform illumination and water vapor. An MV-CA003-21UM Hikvision industrial camera was used to capture vibration videos of the simply supported beam, and the algorithm successfully extracted the global displacement response. The comparison between the identified vibration displacement and the measurements from the eddy current displacement sensor showed an error of approximately 5%, reassuring the algorithm’s accuracy. Moreover, the algorithm accurately identified multiple modal parameters of the simply supported beam, confirming its effectiveness. Finally, this study applied the method to identify vibration displacement and modal parameters of a real reinforced concrete beam, reinforcing its potential application in practical engineering environments.