An improved FFT-based space-time image velocimetry method via flow signal detection in the Fourier spectrum

Abstract This study proposes an improved fast Fourier Transform (FFT)-based space-time image velocimetry (STIV) method for accurate river surface velocity estimation under complex environmental conditions. Based on a comprehensive dataset of real space-time images and their corresponding Fourier spectrum images from diverse river scenes, a you only look once-FSD object detection model is developed to identify valid flow signals in the frequency domain. The radius of the bounding box’s minimum enclosing circle is used as the angular search radius in FFT-STIV for estimating the main orientation of texture. Invalid velocity lines are interpolated using cross-sectional velocity distribution. The model achieves 97.70% Precision and 96.91% Recall on the validation set, representing a notable improvement over the baseline model. In two representative cases, the proposed method reduces the average relative error of FFT-STIV from 49.47% to 7.48% and from 91.88% to 6.15% respectively. By combining the powerful feature extraction capabilities of deep learning with the high resolution and interpretability of FFT, this method demonstrates superior robustness and measurement accuracy.