A method of applying deep learning based optical flow algorithm to river flow discharge measurement

Abstract

River flow discharge monitoring is one of the critical tasks performed at hydrological stations. The large-scale particle image velocimetry (LSPIV) method widely used in hydrological stations is often limited by a lack of floating objects and has a high computational complexity. The space-time image velocimetry method is susceptible to noise interference and requires high stability of the flow over time. This paper proposes a flow measurement method based on the recurrent all-pairs field transforms for optical flow (RAFT) algorithm. The 4D correlation volume pyramid in the RAFT network structure can effectively handle changing and complex flow conditions. The convolutional block attention module is introduced into the optical flow update module after the 4D correlation volume pyramid, enhancing the ability to capture complex flow surface information. Additionally, feature extraction adds deformable convolution to expand the receptive field of the flow image, which has better adaptability in non-rigid motion. To validate the effectiveness of the new method (RAFT-D-C), this paper conducts comparative experiments with both existing and new methods. The experimental results show that RAFT-D-C has relative errors of 2.13% and 4.41% for the average flow velocity of two rivers and relative errors of 2.19% and 3.05% for the total discharge, respectively. RAFT-D-C provides improved accuracy compared to other methods and requires less computational run time than the frequently used LSPIV method.