# River Surface Space-Time Image Velocimetry Based on Dual-Channel Residual Network
###### Abstract  
Space-Time Image Velocimetry (STIV) estimates the one-dimensional time-averaged velocity by analyzing the main orientation of texture (MOT) in space-time images (STIs). However, environmental interference often blurs weak tracer textures in STIs, limiting the accuracy of traditional MOT detection algorithms based on shallow features like images' gray gradient. To solve this problem, we propose a deep learning-based MOT detection model using a dual-channel ResNet (DCResNet). The model integrates gray and edge channels through ResNet18, performs weighted fusion on the features extracted from two channels, and finally outputs the MOT. An adaptive threshold Sobel operator in the edge channel improves the model's ability to extract edge features in STI. Based on a typical mountainous river (located at the Panzhihua hydrological station in Panzhihua City, Sichuan Province), an STI dataset is constructed. DCResNet achieves the optimal MOT detection at a 7:3 gray-edge fusion ratio, with MAEs of 0.41\({}^{\circ}\) (normal scenarios) and 1.2\({}^{\circ}\) (complex noise scenarios), respectively, outperforming the single-channel models. In flow velocity comparison experiments, DCResNet demonstrates an excellent detection performance and robustness. Compared to current meter results, the MRE of DCResNet is 4.08%, which is better than the FFT method. r 2025 20  
STIV directly estimates the one-dimensional time-averaged flow velocity by detecting the main orientation of texture (MOT) of the space-time image (STI), and it was initially proposed by [PERSON] et al. [10]. Owing to its high spatial resolution and real-time processing capabilities, STIV is particularly suitable for bank-based online flow measurement systems captured at small tilt angles [11]. Currently, it has been developed into software, including KU-STIV [12], Hydroview [13], AIFlow [14], and Hydro-STIV [15], which are widely used for online flow monitoring in small and medium-sized rivers. The key to STIV is the accurate detection of MOT. Traditional MOT detection methods primarily include the Gradient Tensor (GT) [11], Two-Dimensional Autocorrelation Function (QESTA) [16], and Fast Fourier Transform (FFT) [17]. Due to the complexity and variability of natural environments, factors such as changing lighting conditions, water surface reflections, obstacles, rain, and vortexes can significantly affect the textures in STIs. When the STI is heavily impacted by noise and the flow signal formed by tracers is weak, the detection results of the above traditional methods often have gross errors. To address these issues, the following noise reduction methods have been proposed: (1) Partition weighted average method [11]. First, the STI is divided into several windows to detect the MOT separately. To quantify the texture clarity in each window, the coherency is introduced. The average MOT is calculated by coherency weighting to reduce the impact of unclear windows. (2) Standardization (STD) filtering method [16]. Calculating standard deviations for each vertical array can equalize the unevenly distributed image intensity in STIs. (3) Edge detection method [17]. Operators like Canny and Sobel are used to extract edge information that reflects the texture orientation of the image. This method suppresses the low-frequency noise and enhances the texture features. (4) Frequency domain filtering method [18; 19]. By applying a fan-type filter to the magnitude spectrum image (MSI), interference noise can be removed, while the signal related to velocity is retained. However, parameters such as radius, direction, and bandwidth are sensitive in different cases, which potentially causes random errors [20]. In recent years, the rapid advancement in deep learning has led to the development of numerous high-performance neural network models. Capable of extracting highly abstract data representations, these models excel at learning intricate mapping relationships and generating precise predictions [21; 22; 23].