## 4 Experiment
### Comparison of Performance with Advanced Counterparts in Similar Modules  
To compare the performance of our designed module with advanced counterparts in similar modules, we conducted a comparative experiment. In this experiment, the backbone was replaced with DDRNet-s and LDMNet, and different Mamba scanning mechanisms were tried at the Neck position, each paired with RAM and AFF in a cross combination. The experimental results are presented in Tables 7 and 8.  
Regarding the Recombine Attention Module, in LDMNet, we introduce the Recombine Attention Module (RAM) to address the issues of feature imbalance and mismatch that arise during the fusion of branches with high-resolution differences. As indicated by the data in Tables 7 and 8, when all other module configurations remain the same, substituting the fusion strategy with RAM yields an increase in mIoU of 0.69% to 1.34% and in mPA of 0.38% to 0.66%, with a reduction in the number of parameters of 0.13 k. Specifically, when using the DDRNet-s-Mamba-AFF combination as the baseline, under the same experimental conditions, replacing AFF with RAM results in an increase in mIoU of 1.34% and mPA of 0.59%; if the backbone remains unchanged while both the Mamba scanning mechanism and RAM are replaced, mIoU and mPA increase by 1.43% and 0.71%, respectively; finally, when the Backbone is replaced with LDMNet, comparing LDMNet-Mamba-RAM with DDRNet-s-Mamba-AFF, mIoU and mPA increase by 1.63% and 0.95%, respectively. This demonstrates that RAM is capable of better adaptive selection and retention of more image features during feature fusion, and it exhibits greater applicability in tasks that require important detailed information such as edge textures.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Params** \\ \hline AFF [28] & 39.71 k \\ RAM & 39.58 k \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the number of parameters between RAM and AFF [28], showing that RAM has fewer parameters than AFF.  
Figure 8: Visualizes the inference of LDMNet compared to BisenetV2 and DDRNet-s on the Water Segmentation in the USVInland for water–shore segmentation. We have marked the detailed differences with boxes and arrows. It can be seen that in complex environments where the reflection of the water surface is overcome, LDMNet still demonstrates high accuracy in demarcating the boundaries between the water and the shore.  
Regarding the Hilbert Curve Scan, we designed four sets of experiments in conjunction with different backbones, as shown in Table 8. SS denotes sequential scanning, SS2D [53] represents the 2D Selective Scan Module, OSSM [57] represents the Omnidirectional Selective Scan Module, and HCS denotes the Hilbert Curve Scan. The results indicate that using SS as the baseline, SS2D improves mIoU by 0.36% and 1.22% through sequential scanning from four directions of the image, while OSSM, using eight-directional sequential scanning, increases mIoU by 0.55% and 0.94%. It is evident that merely increasing the number of directions not only boosts computational load but also does not necessarily lead to enhanced precision. Our proposed method of processing multi-scale images through HCS and stacking them results in an improvement in mIoU of 2.09% and 2.04%. Therefore, to enhance the model's effective modeling of non-causal data, it is crucial to select an appropriate scanning method. Our scanning method yielded more convincing results when combined with the Deep Dual-branch Network and Mamba.