# River Surface Space-Time Image Velocimetry Based on Dual-Channel Residual Network
## 4 Experiments and Discussions
### MOT Detection Comparison Experiments  
A comparison is conducted between the dual-channel network and single-channel networks (Gray-Channel and Edge-Channel). Tables 2 and 3 give the MAE and SD for the three models. The overall MAE of Gray-Channel is 0.05\({}^{\circ}\) lower than that of Edge-Channel, while it exhibits a 0.42\({}^{\circ}\) higher SD. These results suggest that although Gray-Channel provides a slightly higher average accuracy, its detection results are more volatile. In contrast, Edge-Channel achieves a better trade-off between accuracy and robustness. Analyzing Table 3, Edge-Channel outperforms Gray-Channel in normal, obstacle, flare, and vortex scenarios, indicating that the STIs with Sobel detection can obtain more prominent features related to the MOT. So it is easier for the model to learn effective texture features. However, in the rain scenario, the texture features of some STIs are highly blurred. The edge information is not obvious, resulting in the loss of texture features after Sobel detection and the high MAE of Edge-Channel. The proposed Dual-Channel demonstrates better detection  
Figure 12: The results of the model’s detection accuracy.  
accuracy compared to the single-channel models. Specifically, Dual-Channel achieves a 0.18\({}^{\circ}\) reduction in MAE relative to Gray-Channel, representing a 21.9% improvement in detection accuracy. This enhancement is consistently observed across all the tested scenarios. The performance gain suggests that the weighted fusion of two channels effectively obtains the features conducive to MOT detection, thereby optimizing DCResNet's detection capability. Furthermore, the smaller SD of the Dual-Channel model indicates greater stability in detection performance, with the narrowest fluctuation range in absolute error distribution.  
To comprehensively demonstrate the detection accuracy of different models, the absolute errors of the three models under the Panzhihua dataset are statistically analyzed according to the threshold. The results are presented in Figure 13, illustrating the proportion of STIs with MOT detection result errors below the specified threshold of absolute error. Overall, Dual-Channel demonstrates good performance, with 94.5% of its detection results maintaining absolute errors below 2\({}^{\circ}\), which is higher than the other two single-channel models, indicating that DCResNet has fewer gross errors and better stability.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Regression Model** & **Dual-Channel** & **Edge-Channel** & **Gray-Channel** \\ \hline MAE & 0.64 & 0.87 & 0.82 \\ SD & 0.71 & 1.01 & 1.43 \\ \hline \hline \end{tabular}
\end{table}
Table 2: MAE and SD results of 3 models on Panzhihua dataset (\({}^{\circ}\)).  
Figure 13: Absolute error distribution of detection results for 3 models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Dual-Channel** & **Edge-Channel** & **Gray-Channel** \\ \hline normal & 0.41 & 0.61 & 0.65 \\ vortex & 1.10 & 1.14 & 1.15 \\ flare & 0.57 & 0.81 & 0.82 \\ obstacle & 0.33 & 0.34 & 0.44 \\ rain & 1.17 & 1.50 & 1.36 \\ \hline \hline \end{tabular}
\end{table}
Table 3: MAE results of 3 models under different scenarios (\({}^{\circ}\)).