Image-based river flow measurement methods have been attracting attention because of their ease of use and safety. Among the image-based methods, the space-time image velocimetry (STIV) technique is regarded as a powerful tool for measuring the streamwise flow because of its high measurement accuracy and robustness. However, depending on the image shooting environment such as stormy weather or nighttime, the conventional automatic analysis methods may generate incorrect values, which has been a problem in building a real-time measurement system. In this study, we tried to solve this problem by incorporating the deep learning method, which has been successful in the field of image analysis in recent years, into the STIV method. The case studies for the three datasets indicated that deep learning can improve the efficiency of the STIV method and can continuously improve performance by learning additional data. The proposed method is suitable for building a real-time measurement system because it has no tuning parameters that need to be adjusted according to the shooting conditions and the calculation speed is fast enough for real-time measurement.

中文翻译：

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通过深度学习提高时空图像测速 (STIV) 的准确性和鲁棒性

基于图像的河流流量测量方法因其易用性和安全性而备受关注。在基于图像的方法中，时空图像测速（STIV）技术因其高测量精度和鲁棒性而被认为是测量流向流动的有力工具。然而，根据图像拍摄环境，例如暴风雨天气或夜间，传统的自动分析方法可能会产生不正确的值，这一直是构建实时测量系统的一个问题。在这项研究中，我们试图通过将近年来在图像分析领域取得成功的深度学习方法纳入 STIV 方法来解决这个问题。三个数据集的案例研究表明，深度学习可以提高 STIV 方法的效率，并且可以通过学习额外的数据来不断提高性能。该方法没有需要根据拍摄条件调整的调谐参数，计算速度足够快，适合实时测量系统的构建。