Computer Vision-Based Structural Displacement Monitoring and Modal Identification with Subpixel Localization Refinement

In conventional structural health monitoring (SHM), the installation of sensors and data acquisition devices will affect the regular operation of structures to a certain extent and is also expensive. In order to overcome these shortcomings, the computer vision- (CV-) based method has been introduced into SHM, and its practical applications are increasing. In this paper, CV-based SHM methods such as template matching and Hough circle transform are described. In order to improve the accuracy of pixel localization, the subpixel localization refinement method is introduced. The displacement monitoring experiment of an aluminum alloy cantilever with three targets is conducted by using the two CV-based SHM methods and the laser displacement sensors simultaneously. The displacement monitoring results of CV-based methods agree well with those measured by the laser transducer system in the time domain. After that, the first two modes of the cantilever are identified from the monitoring results. In addition, the experimental modes identified from the monitoring data and those calculated from the finite element model are also consistent. Therefore, the developed CV-based methods can obtain accurate displacement results in both time and frequency domains, which could be applied to complex structures with more monitoring targets.

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.