## 5 Analysis of Results and Field Testing
### Field Tests  
#### 5.5.1 Water Level Line Detection Test and Analysis  
This paper utilizes the Average Pixel Absolute Error (APAE) as the evaluation index for water level line detection in order to further validate the effectiveness of the SegFormer-UNet segmentation network. The mathematical expression for APAE is defined as Equation (10). \[APAE=\frac{1}{n}\sum_{i=0}^{n}\lvert y_{i}-y_{i}^{\prime}\rvert, \tag{10}\]  
where \(n\) denotes the total number of images tested, \(y_{i}\) denotes the pixel vertical coordinate of the water level line detected by the algorithm for the \(i\)-th image, and \(y_{i}^{\prime}\) denotes the pixel coordinate of the actual water level line in the \(i\)-th image calibrated manually. In this study, four water level detection methods were compared, to quantitatively assess the accuracy of water level line detection, and the results are displayed in Table 5. The comparison of the data in the table shows that this approach has the lowest average absolute pixel error among the five methods, which may avoid the negative impact of water gauge information loss on water level detection. The assessment index demonstrates that SegFormer-UNet can accurately segment the water body and background region in the water gauge images, and that the average absolute pixel error in water level line detection is 1.73 pixels, which is sufficient to meet the criteria for water level line detection accuracy. In Table 5, it is evident that the average absolute pixel error for water level line detection in this paper is significantly lower than those of other water level detection algorithms. This is due to two reasons. Firstly, the water gauge images used in this paper are actual site images, which include a small number of night images and missing water gauge features. Secondly, the proposed algorithm in this paper does not rely on the water gauge itself after establishing the mapping relationship, which substantially eliminates the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Architecture**} & \multirow{2}{*}{**Params (M)**} & \multirow{2}{*}{**GFLOPs (G)**} & \multirow{2}{*}{**mIoU (\%)**} & \multirow{2}{*}{**mPA (\%)**} \\ \cline{3-3} \cline{5-8}  & **Stage 1** & & & & & & & \\ \hline \multirow{4}{*}{SegFormer-UNet-B0} & CA & CA & CA & CA & 6.17 & 15.41 & 94.26 & 96.95 \\  & CA & CA & CA & TA & 6.24 & 15.43 & 96.65 & 97.56 \\  & CA & CA & TA & TA & 5.54 & 14.49 & 96.76 & 98.29 \\  & CA & TA & TA & TA & 5.58 & 14.07 & 96.69 & 98.22 \\ \hline \multirow{4}{*}{SegFormer-UNet-B1} & CA & CA & CA & CA & 16.76 & 25.76 & 95.67 & 97.73 \\  & CA & CA & CA & TA & 17.18 & 25.87 & 97.06 & 98.16 \\  & CA & CA & TA & TA & 15.78 & 23.50 & 97.14 & 98.46 \\  & CA & TA & TA & TA & 16.30 & 23.42 & 97.12 & 98.42 \\ \hline \multirow{4}{*}{SegFormer-UNet-B2} & CA & CA & CA & CA & 29.59 & 38.97 & 95.70 & 97.67 \\  & CA & CA & CA & TA & 30.20 & 39.13 & 97.16 & 98.47 \\  & CA & CA & TA & TA & 26.00 & 32.04 & 97.43 & 98.62 \\  & CA & TA & TA & TA & 26.80 & 31.90 & 97.37 & 98.61 \\ \hline \multirow{4}{*}{SegFormer-UNet-B3} & CA & CA & CA & CA & 47.93 & 59.74 & 96.21 & 97.70 \\  & CA & CA & CA & TA & 48.55 & 59.90 & 97.26 & 98.57 \\ \cline{1-1}  & CA & CA & TA & TA & 46.56 & 49.43 & 98.34 & 99.10 \\ \cline{1-1}  & CA & TA & TA & TA & 46.67 & 46.55 & 97.27 & 98.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation studies related to Encoder Structure. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **Average Absolute Pixel Error (Pixels)** \\ \hline [PERSON] [16] & 38.32 \\ [PERSON] [35] & 10.47 \\ [PERSON] [11] & 13.06 \\ [PERSON] [18] & 5.02 \\ [PERSON] & 1.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average absolute pixel error. negative impact caused by missing water gauge features.