The determination of river velocity is important for hydromorphological analyses and river monitoring systems. Indirect measurements of river velocity using videos recorded by unmanned aerial vehicles (UAV) allow fast and cost-effective processing of information about the river stretch. This paper presents a method for computing flow velocity of the river surface using deep supervised model RAFT to determine the optical flow in combination with image pre-processing by convolutional operations. Moreover, the windiness coefficients and variance score were proposed to evaluate reliability of the collected data and the obtained results of optical flow detection. Various image pre-processing techniques were applied, namely the selection of the analysed area and the number of convolutional operations to select the one with the lowest variance score. This score represents the consistency of the river flow velocity during the video and can be used to filter out unreliable results. The numerical experiments were performed using the videos and directly measured velocity values of 4 shallow rivers in Lithuania collected during the field surveys. The optical velocity estimation method showed good correspondence to the directly measured values for the velocity range from 0 m/s to 0.8 m/s in the points with low variance score up to 0.192 that represents the first quartile of the variance. The optical flow method tends to underestimate the velocity up to 0.5 m/s for the quartiles with the higher variance scores. It was shown that in most cases the lowest variance score value was obtained using pre-processing techniques without convolutional operations. However, the need to analyse various pre-processing techniques arises from the different origin of the objects moving on the river surface.

中文翻译：

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采用光学识别的基于无人机的河流流速测定框架


河流流速的确定对于水貌分析和河流监测系统非常重要。使用无人机 (UAV) 录制的视频间接测量河流流速，可以快速且经济高效地处理有关河流河段的信息。本文提出了一种利用深度监督模型 RAFT 确定光流并结合卷积运算进行图像预处理来计算河流表面流速的方法。此外，提出了风度系数和方差分数来评估收集的数据和获得的光流检测结果的可靠性。应用了各种图像预处理技术，即选择分析区域和卷积运算的数量，以选择方差分数最低的区域。该分数代表了视频期间河流流速的一致性，可用于过滤掉不可靠的结果。数值实验利用现场调查过程中收集的立陶宛4条浅水河流的视频和直接测量的流速值进行。光学速度估计方法在 0 m/s 至 0.8 m/s 的速度范围内，在方差分数低至 0.192（代表方差的第一个四分位数）的点上显示出与直接测量值的良好对应性。对于具有较高方差分数的四分位数，光流方法往往会低估高达 0.5 m/s 的速度。结果表明，在大多数情况下，使用预处理技术无需卷积运算即可获得最低方差得分值。 然而，由于河面上运动物体的来源不同，需要分析各种预处理技术。