# River Surface Space-Time Image Velocimetry Based on Dual-Channel Residual Network
## 4 Experiments and Discussions
### Surface Velocity Comparison Experiments
#### 4.3.2 Test 1: Sunny Day at Panzhihua Station  
The collection time of the river video under sunny conditions is 9 a.m. on 29 July 2020, and the duration of the video is 30 s, as shown in Figure 14. The flow direction of the river is from left to right. The flow tracing conditions are disturbed by many factors, such as vortex, obstacle, and flare. The most obvious interference factor is flare. The cross-section of the flow measurement is a rectangular frame area. Nine testing lines with a starting distance from 45 m to 160 m were measured, including vortex (1, 8, 9), obstacle (1), normal (2-5), and flare (6-9) scenarios. The corresponding STIs are shown in  
The MOT and surface velocity results obtained by the three methods are shown in Tables 4 and 5. Label values are used as a reference. The label values in Table 4 are the MOT results obtained using the manual labeling method in Section 3.1, and the label values in Table 5 are the surface velocity results calculated by the corresponding MOT. From the MOT detection results in Table 4, the AE on testing lines 2-7 is the smallest, while that of testing lines 1, 8, and 9 is larger. Testing line 1 is located in the near field, which is affected by the near-field vortex and the occlusion of the pipeline, so the detection error of the two methods is slightly larger. Testing lines 2-5 are less disturbed, the textures are clear, and the orientations are consistent. The interference caused by the flare is superimposed in the vertical orientation of testing lines 6 and 7, but the overall texture feature is still obvious. Therefore, FFT and DCResNet can give a more accurate MOT for the STIs corresponding to the testing lines 2-7. Testing lines 8 and 9 are affected by flare and vortex, resulting in a sharp decline in the texture clarity of the STI. The AEs of the FFT method are 2\({}^{\circ}\) and 24.84\({}^{\circ}\), respectively, which shows large errors. However, DCResNet can extract effective texture features and has a certain degree of robustness to noise by training on the Panzhihua dataset, which gives a more reliable MOT detection. The AE of the MOT is controlled within 2\({}^{\circ}\).  
\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 (\({}^{\circ}\))**} \\  & **Distance (m)** & **(\({}^{\circ}\))** & **(\({}^{\circ}\))** & **(\({}^{\circ}\))** & **FFT** & **DCResNet** \\ \hline
1 & 45 & 81.45 & 80.30 & 80.89 & 0.56 & 0.59 \\
2 & 65 & 81.97 & 82.22 & 82.20 & 0.23 & 0.02 \\
3 & 75 & 80.57 & 80.63 & 80.80 & 0.23 & 0.17 \\
4 & 85 & 80.01 & 80.35 & 80.48 & 0.47 & 0.13 \\
5 & 95 & 78.95 & 78.62 & 78.80 & 0.15 & 0.18 \\
6 & 115 & 77.39 & 77.48 & 77.52 & 0.13 & 0.04 \\
7 & 130 & 75.43 & 74.56 & 74.45 & 0.98 & 0.11 \\
8 & 145 & 68.10 & 66.30 & 66.10 & 2.00 & 0.20 \\
9 & 160 & 18.55 & 41.50 & 43.39 & 24.84 & 1.89 \\ \hline \hline \end{tabular}
\end{table}
Table 4: MOT detection results under sunny conditions.  
Figure 15: STIs under sunny conditions. (1–9 correspond to the starting distances of 45 m, 65 m, 75 m, 85 m, 95 m, 115 m, 130 m, 145 m, 160 m, respectively.)  
The corresponding surface velocity distribution is shown in Figure 16. Overall, the surface velocity results obtained by DCResNet are closer to the label values, and the MRE is 3.01%, which has good robustness. However, in the scenario disturbed by multiple noises, the detection results of FFT have a large deviation, which leads to unreliable surface velocity values.