School of Civil Engineering, Dalian University of Technology, Dalian 116024, China.
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel). 2020 May 15;20(10):2805. doi: 10.3390/s20102805.
The objective of this study is to evaluate and improve the accuracy and stability of a strain measurement method that uses the speeded-up robust feature (SURF) method to trace the displacement of feature points in microimages and obtain the strain in objects. The microimages were acquired using a smartphone with a portable microscope, which has a broad prospect of application. An experiment was performed using an unpacked optical fiber as the experimental carrier. The matching effect of the SURF method was analyzed in the microimage, and the M-estimator sample consensus (MSAC) algorithm was used to reject outliers generated by SURF. The results indicated that the accuracy of strain measurement using the proposed method is improved by modifying the feature point tracking method and measurement method. When compared with the fiber Bragg grating (FBG) data, the maximum standard error corresponded to 2.5 με, which satisfies the requirement of structural health monitoring (SHM) in practical engineering.
本研究旨在评估和提高一种应变测量方法的准确性和稳定性，该方法使用加速稳健特征（SURF）方法来跟踪微图像中特征点的位移，并获得物体中的应变。微图像是使用带有便携式显微镜的智能手机获取的，这种显微镜具有广阔的应用前景。实验使用未封装的光纤作为实验载体。分析了微图像中 SURF 方法的匹配效果，并使用 M 估计样本一致（MSAC）算法剔除 SURF 生成的异常值。结果表明，通过改进特征点跟踪方法和测量方法，提高了所提出方法的应变测量精度。与光纤布拉格光栅（FBG）数据相比，最大标准误差对应于 2.5 με，满足实际工程结构健康监测（SHM）的要求。
