# Estimation of River Velocity and Discharge Based on Video Images and Deep Learning
## 1 Introduction
[10] proposed to detect the texture angle by calculating the Two-dimensional Autocorrelation Function (QESTA) of the image intensity in the space-time image, where the gradient of the region with high correlation indicates the effective texture direction, from which the texture angle is derived. [PERSON] et al. [11] introduced a frequency domain STIV method based on Fast Fourier Transform (FFT), which leveraged the orthogonality between the main orientation of texture (MOT) and the main orientation of spectrum (MOS); the MOT was ascertained by identifying the MOS. However, these methods are extremely sensitive to noise, which not only reduces the accuracy of measurement, but also makes it difficult to take into account applications in various complex environments. In order to improve the detection accuracy of MOT, [PERSON] et al. [12] proposed a new denoising method that combines frequency domain filtering technology, which generated a noise-free space-time image with a clear texture, and was applied to the measurement of river velocity. [PERSON] et al. [13] firstly performed preprocessing operations on space-time images, primarily including contrast enhancement and noise filtering, with residual noise further mitigated by frequency domain filtering techniques to obtain precise texture angle. [PERSON] et al. [14] proposed the OT-STIV-SC algorithm, which effectively enhanced the trajectory texture information of space-time images and integrated statistical features to detect texture angles. Methods for detecting the space-time image texture angle based on deep learning have been highly favored by hydrological researchers in recent years. Researchers like [PERSON] al. [15] combined deep learning with STIV to input STIs into a CNN network, and it demonstrated a high STI texture angle recognition accuracy in validation experiments with both synthetic and real datasets. [PERSON] et al. [16] employed residual networks to construct regression models to detect MOT values. Furthermore, [PERSON] et al. [17] enhanced the accuracy of texture angle recognition by integrating the original STIs into a depth residual network with global concern of relational perception for training. While these methods have improved the robustness of measurement techniques, the lack of publicly available datasets and the demand for a considerable number of network parameters and calculations remain obstacles to be addressed. Traditional STIV generates STIs that contain a large amount of noise in complex scenes, which means it is difficult to directly and accurately determine the texture orientation. It often requires enhancement and filtering operations to obtain images with clear texture trajectories, but these processes rely heavily on manual parameter settings and are unable to adjust parameters adaptively for different scenes. This paper introduces a novel space-time image texture angles measurement method in the frequency domain to avoid the complex filtering steps and address the issue of larger angle detection errors in traditional STIV in complex noisy environments. The problem of space-time images texture angle detection is regarded as an image classification problem. It is necessary to establish frequency domain image datasets containing multiple scenarios to compensate for the shortage of datasets, and this paper then utilizes ShuffleNetV2 to construct a classification model for training and then predicts texture angle values using a saved optimal weight file. The whole measurement process does not require parameter optimization and the improved ShuffleNetV2 retains lightweight characteristics compared with the CNN and residual networks in previous studies. Finally, the river mean velocity and discharge are calculated based on the detected angle values and the velocity-area method.