Video velocity measurement: A two-stage flow velocity prediction method based on deep learning

Abstract

Due to the uncertainty in output caused by environmental changes, significant discrepancies are expected between the surface flow velocities predicted using deep learning methods and the instantaneous flow velocities. In this paper, a two-stage deep learning flow velocity measurement algorithm is proposed. During the external calibration process, the upper and lower frames of the recorded water flow video are cyclically traversed to acquire predicted flow velocity values using the deep learning velocity measurement algorithm. Meanwhile, the pixel displacement is obtained using the sparse optical flow tracking method and then post-processed to derive the velocity calibration value and pixel calibration value. During the detection process, the deep learning-predicted flow velocity is internally calibrated using the velocity calibration value and the pixel calibration value to adapt to changes in water flows. Compared with the pre-improved algorithm, the method achieves the minimum root mean square error in five different flow velocity videos and maintains high accuracy when the flow velocity changes rapidly. The obtained results are very promising and can help improve the reliability of video flow rate assessment algorithms. HIGHLIGHTS New methods are needed to ensure the instability of the video-based surface velocity prediction due to environmental changes.; Avoid direct measurement of high velocity, using the slow to fast process to indirectly get accurate high velocity.; We provide a river data set for deep learning model training, which is convenient for researchers interested in this to expand their research.;

Keywords

• deep learning
• external calibration
• flow velocity
• sparse optical flow tracking
• two-stage