Micro-amplitude vibration measurement using vision-based magnification and tracking

A new deep learning-based vision measurement method is proposed to accurately measure the micro-vibration displacements of objects in different illuminations and backgrounds. The measurement method pre-process the video, then the deep learning correlation methods are used to zoom in the target object and track the vibration trajectory, and the pixel displacement is converted to actual displacement by pixel equivalents. By comparing the three sets of experiments, the proposed method has exceptional accuracy. When measuring vibration displace-ment of 0.1 mm, the Root Mean Square Error (RMSE) and Normalized Root Mean Square Error (NRMSE) are 0.0234 mm and 11.8601 %. By comparing with the Rectangle Detection Algorithm and the Template Matching Algorithm, the proposed algorithm outperforms these two traditional methods, especially for the complex en-vironments. It can be concluded that this method, as a new visual measurement method, can be adapted to a variety of complex environments and can accurately measure micro-amplitude vibrations.

Automated summary

A new deep learning-based vision measurement method is proposed to accurately measure micro-vibration displacements of objects under different illuminations and backgrounds. The method preprocesses the video, applies deep learning correlation techniques to zoom in on the target object and track its vibration trajectory, and converts pixel displacement to actual displacement. Experimental results demonstrate that the proposed method outperforms traditional methods, especially in complex environments, achieving exceptional accuracy.