# River Surface Space-Time Image Velocimetry Based on Dual-Channel Residual Network
## 4 Experiments and Discussions
### Surface Velocity Comparison Experiments
#### 4.3.3 Test 2: Rainy Day at Panzhihua Station  
The river video is captured at 9:00 on 24 August 2020, under rainy conditions. The river cross-section is shown in Figure 17. The current shooting conditions are harsh; the light is dim and affected by rain. The visibility of water flow tracers is poor, measuring nine testing lines from a starting distance of 50 m to 160 m. It mainly includes vortex (1, 8, 9) and rain (1-9) scenarios. The corresponding STIs are shown in Figure 18.  
The results of MOT and surface velocity obtained are shown in Tables 6 and 7. Notably, at the testing lines (1, 8, 9), the STIs suffer from severe texture degradation due to rain and vortex interference. This dual disturbance manifests as both feature blurring and an inconsistent texture orientation. For the FFT, which depends on texture clarity, the less effective texture features result in AEs of 54.07\({}^{\circ}\), 10.78\({}^{\circ}\), and 13.95\({}^{\circ}\) in the MOT detection results. The results underscore the challenge of frequency-domain analysis under poor texture conditions. DCResNet demonstrates robust performance under challenging conditions due to its enhanced feature extraction capability. Although the texture features are weak, DCResNet can still extract effective texture features and then learn the complex nonlinear mapping function from texture features to angle space. This enables accurate MOT detection, with the AE constrained below 2.25\({}^{\circ}\).  
Figure 16: Surface velocity distribution of test 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Starting** & **FFT** & **DCResNet** & **Label Value** & \multicolumn{2}{c}{**RE (\%)**} \\ \cline{3-7}  & **Distance (m)** & **(m/s)** & **(m/s)** & **(m/s)** & **FFT** & **DCResNet** \\ \hline
1 & 45 & 2.48 & 2.18 & 2.33 & 6.44 & 6.44 \\
2 & 65 & 3.91 & 4.05 & 4.03 & 2.98 & 0.50 \\
3 & 75 & 3.92 & 3.94 & 4.01 & 2.24 & 1.75 \\
4 & 85 & 4.15 & 4.30 & 4.36 & 4.82 & 1.38 \\
5 & 95 & 4.16 & 4.04 & 4.11 & 1.22 & 1.70 \\
6 & 115 & 4.29 & 4.32 & 4.34 & 1.15 & 0.46 \\
7 & 130 & 4.20 & 3.95 & 3.92 & 7.14 & 0.77 \\
8 & 145 & 3.08 & 2.96 & 2.79 & 10.39 & 6.09 \\
9 & 160 & 0.44 & 1.15 & 1.25 & 64.8 & 8.00 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Surface velocity results under sunny conditions.  
Figure 17: Section diagram of test 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Starting** & **FFT** & **DCResNet** & **Label Value** & \multicolumn{2}{c}{**AE (\({}^{\circ}\))**} \\ \cline{2-7}  & **Distance (m)** & **(\({}^{\circ}\))** & **(\({}^{\circ}\))** & **(\({}^{\circ}\))** & **FFT** & **DCResNet** \\ \hline
1 & 50 & 23.95 & 77.11 & 78.02 & 54.07 & 0.91 \\
2 & 60 & 80.44 & 79.69 & 80.01 & 0.43 & 0.32 \\
3 & 75 & 80.42 & 80.62 & 80.78 & 0.36 & 0.16 \\
4 & 85 & 79.31 & 78.60 & 78.31 & 1.00 & 0.29 \\
5 & 95 & 78.50 & 79.03 & 78.75 & 0.25 & 0.28 \\
6 & 105 & 77.02 & 77.39 & 77.21 & 0.19 & 0.18 \\
7 & 120 & 74.55 & 75.02 & 74.68 & 0.13 & 0.34 \\
8 & 140 & 76.12 & 67.59 & 65.34 & 10.78 & 2.25 \\
9 & 160 & 23.30 & 39.12 & 37.25 & 13.95 & 1.87 \\ \hline \hline \end{tabular}
\end{table}
Table 6: MOT detection results under rainy conditions.  
Figure 18: STIs under rainy conditions. (1–9 correspond to the starting distances of 50 m, 60 m, 75 m, 85 m, 95 m, 105 m, 120 m, 140 m, 160 m, respectively.)  
Figure 19 shows the surface velocity distribution. It can be seen that the FFT and DCResNet surface velocity distributions on the testing lines (2-7) with better tracer conditions are close to the label values. At this time, the RE of the surface velocity of DCResNet can be controlled within 3.80%. For testing lines (1, 8, 9), which are greatly affected by noise, the surface velocity results of FFT show abnormal values, while DCResNet can give more accurate velocity values. The MRE of DCResNet in this test is 4.50%, which is 22.39% less than FFT, verifying the robustness of DCResNet in noisy scenarios.