# Water Segmentation for Unmanned Ship Navigation Based on Multi-Scale Feature Fusion
## 4 Experimental Results and Analysis
### Results and Analysis of Ablation Experiments  
In order to validate the effectiveness of the proposed Fuse-ResNet method for water segmentation, we conducted ablation experiments focusing on testing the role of different modules within Fuse-ResNet. These experiments aimed to thoroughly analyze the contribution of each module to the overall performance and explore their practical effects in various tasks. To this end, we designed five experiments to test the specific functions and interactions of the CBAM, FPN, and ASPP modules within the Fuse-ResNet model. All comparative experiments were conducted under the same experimental environment and parameter configuration to ensure the fairness and consistency of the results. The experimental results are presented in Table 2, which clearly shows the impact of each module on the model's performance. In Table 2, Experiment 5 shows the results of the proposed Fuse-ResNet method on the dataset, with mIoU and mPA reaching 98.45% and 99.25%, respectively. In Experiment 1, after removing the attention-enhanced module (CBAM) from the model encoder, the mIoU and mPA decreased by 0.45% and 0.17%, respectively, compared to Experiment 5. This indicates that the attention-enhanced module helps the model capture key target features more effectively and reduces background noise interference, thereby improving the model's recognition and segmentation performance in complex scenes. This result further demonstrates the importance of the attention mechanism in handling complex scenes, especially when background noise is significant. The relatively slight performance degradation when removing the attention-enhanced module suggests that the model's backbone network (ResNet-50) is capable of capturing key features at a sufficiently high level, even without the attention mechanism, allowing it to maintain most of its recognition capabilities. In Experiment 2, after removing multi-scale feature fusion (FPN) and segmenting only using high-level semantic features through the ASPP module, the model lost its ability to capture objects of different scales. The mIoU and mPA decreased by 0.65% and 0.22%, respectively, compared to Experiment 3. This highlights the importance of multi-scale feature fusion in effectively supplementing the features of the target, and show how it is crucial for improving segmentation performance. The relatively mild decrease in performance when multi-scale fusion is removed suggests that the model still relies on its high-level semantic features to capture broad contextual information. Even without the multi-scale fusion, the model can still perform well in some aspects of segmentation, especially when the objects of interest are not significantly different in scale. In Experiment 3, after removing the dilated spatial pyramid pooling module (ASPP) and directly performing multi-scale fusion using the features extracted from the backbone network, the mIoU and mPA decreased by 0.3% and 0.16%, respectively, compared to Experiment 5. This suggests that the ASPP module helps to enhance the receptive field of the model, enabling it to better capture multi-scale contextual information in the image. This indicates that the dilated spatial pyramid pooling module enhances the receptive field, improving the model's ability to capture local details. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment Number** & **CBAM** & **Multi-Scale Feature Fusion** & **ASPP** & **mPA/\%** & **mIoU/\%** \\ \hline \�  
In Experiment 1, after removing both the multi-scale (FPN) and dilated spatial pyramid pooling (ASPP) modules, the model encoder used only high-level semantic features for segmentation. The mIoU and mPA decreased by 1.09% and 0.55%, respectively, compared to Experiment 1. This demonstrates that using both multi-scale and dilated spatial pyramid pooling modules significantly improves the model's segmentation performance, and effectively solves the problem of recognizing and segmenting fine boundaries.