In this study, a new strain measuring method was designed based on smartphone and machine vision algorithms, while the automatic calibration of the pixel size of images taken by the smartphone was achieved with the help of circle detection. A piston-type sensor to complement this method was designed, and the static and dynamic experimental results of the sensor matched well with those from FBG. Meanwhile, the sensor was tested with temperature compensation to obtain an empirical equation for the effect of temperature on the sensor. When compared with the FBG data, the static data of the sensor had the mean error of less than 4 με and the standard error of the mean (SEM) of 1.9 με, while the dynamic data had the mean error is less than 4.3 με and the SEM is 5.4 με. The results confirm that the sensor has great potential in structural health monitoring.

本研究基于智能手机和机器视觉算法设计了一种新的应变测量方法，同时借助圆检测实现了智能手机拍摄的图像像素大小的自动校准。设计了一种活塞式传感器来补充该方法，该传感器的静态和动态实验结果与FBG的结果匹配良好。同时，对传感器进行温度补偿测试，得到温度对传感器影响的经验方程。与FBG数据相比，传感器静态数据的平均误差小于4 με，均值标准误差（SEM）为1.9 με，而动态数据的平均误差小于4.3 με， SEM 为 5.4 με。