# Water Segmentation for Unmanned Ship Navigation Based on Multi-Scale Feature Fusion
## 4 Experimental Results and Analysis
### Comparative Experimental Results Analysis  
In this experiment, the segmentation performance of Fuse-ResNet is compared with the mainstream methods such as FCN, U-Net, DeepLabV3, and Uper-Net on the USVInland dataset. All tests were conducted under the same experimental environment and parameter configuration. The water segmentation performance comparison results are shown in Table 1. The Fuse-ResNet results in the table correspond to the experimental results of the network model proposed in this paper. It can be seen that the proposed Fuse-ResNet method outperforms the Uper-Net method, with improvements of 0.24% in mPA and 0.32% in mIoU. Compared to the DeepLabV3 method, the Fuse-ResNet method achieves improvements of 0.15% in mPA and 0.36% in mIoU. When compared to mainstream networks like FCN and U-Net, the Fuse-ResNet method achieves improvements of 0.71% and 1.77% in mIoU, respectively. Although DeepLabV3 and Uper-Net achieve satisfactory mIoU, both methods have relatively larger parameter counts, which leads to slower training and inference. In contrast, the multi-scale feature extraction network design of Fuse-ResNet, utilizing multi-scale feature fusion and spatial pyramid pooling modules, improves segmentation performance, while requiring significantly fewer model parameters than DeepLabV3 and Uper-Net. In order to validate the segmentation performance of Fuse-ResNet in real-world scenarios, four representative images from the USVInland dataset were selected for qualitative result analysis. The visualized segmentation results are shown in Figure 7, where each column consists of three parts: the original image, the segmented image, and an enlarged detail section. The model segments the original image into two categories, with the red color representing the water area and the gray color representing the land or obstacle area. In water scenes 1 and 4 in Figure 7, the FCN network can correctly segment the water area in simple scenarios, but struggles with rougher segmentation boundaries in more complex scenes. This is particularly evident in water surface reflections and fine details, where some water areas are either incorrectly segmented or misclassified as background. In contrast, U-Net demonstrates strong detail capture ability, and can accurately segment the water boundary. However, in complex scenarios like water scenes 2 and 3, U-Net performs less well with water surface reflections and details, showing noticeable over-segmentation or under-segmentation. On the other hand, Deeplabv3 enhances its receptive field through dilated convolutions, improving its segmentation capability for objects of varying scales. Uper-Net excels at capturing and fusing multi-scale features, performing well in segmenting the boundaries of the water surface and the surrounding environment. However, there are still some segmentation details missing or incorrectly segmented in certain water surface regions around the fountain area in water scene 2 of Figure 7.  
In essence, the proposed Fuse-ResNet achieves excellent classification performance across all water scenes through the fusion of multi-scale features and effective integration \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**PA/\%**} & \multicolumn{2}{c}{**IOU/\%**} & \multirow{2}{*}{**mPA/\%**} & \multirow{2}{*}{**mIOU/\%**} & \multirow{2}{*}{**No. of Parameters/M**} \\ \cline{2-2} \cline{5-8}  & **Body of Water** & **Land** & & & & & & \\ \hline FCN & 99.0 & 98.8 & 98.31 & 97.17 & 98.90 & 97.74 & 47.105 \\ U-Net & 98.27 & 98.67 & 97.5 & 95.87 & 98.47 & 96.68 & 28.991 \\ Deeplabv3 & 99.09 & 99.11 & 98.57 & 97.61 & 99.10 & 98.09 & 65.72 \\ Uper-Net & 99.41 & 98.61 & 98.6 & 97.65 & 99.01 & 98.13 & 64.042 \\ Dmnet & 99.22 & 98.82 & 98.53 & 97.53 & 99.02 & 98.03 & 50.803 \\
**Fuse-ResNet** & **99.31** & **99.19** & **98.84** & **98.06** & **99.25** & **98.45** & 63.764 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classical model performance comparison results. of contextual information.