基于机器视觉的无标记图像低频加速度计动态校准方法

Machine vision-based dynamic calibration of low-frequency accelerometers with non-marker images

针对现有的低频加速度计校准方法存在系统复杂、精度有限和校准类型限制的问题,难以满足日益增长的高性能低频加速度计动态校准需求。因此,提出一种基于机器视觉的无标记图像低频加速度计校准方法,通过高精度摄像机拍摄运动序列图像,并建立参考图像与目标图像的感兴趣区域,使用数字图像相关方法进行振动台激励的高精度复现,实现低频加速度计的校准。为验证该方法的有效性和可靠性,与激光干涉法在0.1~10 Hz频率范围内进行对比实验,灵敏度幅值校准平均误差为0.7696%,最大相对误差为1.536%。实验结果表明,机器视觉法可实现在0.1~10 Hz范围内的灵敏度幅值高精度校准。

In response to the existing low-frequency accelerometer calibration methods,there are problems of system complexity,limited accuracy,and calibration type restriction,which make it difficult to meet the increasing demand for dynamic calibration of high-performance low-frequency accelerometers.Therefore,a machine vision-based calibration method for low-frequency accelerometers with non-marker images is proposed,in which a high-precision camera captures motion sequence images and establishes the region of interest between the reference image and the target image,and a digital image correlation method is used to realize a high-precision reproduction of the shaker excitation to achieve the calibration of low-frequency accelerometers.To verify the validity and reliability of the method,comparison experiments are conducted with the laser interferometric method in the frequency range of 0.1-10 Hz,and the average error of the sensitivity amplitude calibration is 0.7696%.The maximum relative error is 1.536%.The experimental results show that the machine vision method can calibrate the sensitivity amplitude in the range of 0.1-10 Hz with high accuracy.