River Surface Velocimetry Based on Virtual River Dataset and Modulated-RAFT

Abstract

Many image-based velocimetry methods, such as large-scale particle image velocimetry (LSPIV), space-time image velocimetry (STIV) and optical flow velocimetry (OFV), have been proposed to estimate river surface velocity (RSV) efficiently and precisely. Among these methods, OFV-based methods have drawn great attention due to its high field resolution and low requirement of tracers. As a powerful approach for estimate optical flow accurately and efficiently, deep optical flow estimation is utilized in OFV-based methods as well. However, these methods often use irrelevant datasets for training due to the immeasurability of optical flow. Models obtained by such approaches suffer from limited generalization because of domain drift. Besides, high similarity of river surfaces often leads to ambiguous correlation volume extracted by deep optical flow estimation model, which can cause mismatch. This work proposed a method for accurately and robustly RSV estimation under velocity range of 0-6.0 m/s. Specifically, we introduced a method for generating relative optical flow datasets and proposed MRAFT, a deep optical flow estimation model combining with correlation volume modulation. Experiment results demonstrate that the generated virtual river datasets effectively improve the generalization of the model and MRAFT remarkably alleviates the mismatch. Our work facilitates the application of deep optical flow on RSV estimation and provides other related researches with optical flow datasets for fine-tuning.