## 4 Experiment
### Speed and Accuracy Comparisons
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Type** & **mIoU (\%)**\(\uparrow\) & **Re (\%)**\(\uparrow\) & **F1**\(\uparrow\) \\ \hline ICNet [54] & CNN & 93.3 & 49.7 & 44.9 \\ STDC2 [55] & CNN & 93.5 & 54.3 & 54.3 \\ PiDNet-s [7] & CNN & 94.1 & 61.8 & 52.2 \\ SFNet [56] & CNN & 95.2 & 62.4 & 58.1 \\ \hline Segmenter [24] & Transformer & 95.1 & 59.5 & 55.2 \\ MLLA [19] & Transformer & 95.3 & 63.5 & 59.4 \\ UNetformer [20] & Transformer & 93.5 & 61.5 & 54.3 \\ TransNeXt-t [53] & Transformer & 94.9 & 61.3 & 53.1 \\ \hline RSMamba [57] & Mamba & 94.2 & 60.1 & 52.9 \\ VM-UNet [14] & Mamba & 95.1 & 64.2 & 61.3 \\ CM-UNet [16] & Mamba & 95.4 & 65.4 & 62.8 \\ VAMamba-s [13] & Mamba & 94.2 & 60.5 & 54.2 \\ LDMNet & CNN\&Mamba & 95.6 & 78.6 & 75.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy comparison of LDMNet with other advanced methods on LaRS. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Resolution** & **mIoU (\%)**\(\uparrow\) & **mDice (\%)**\(\uparrow\) \\ \hline BisenetV2 [58] & 640 \(\times\) 640 & 97.68 & 98.46 \\ DDRNet-s [8] & 640 \(\times\) 640 & 98.64 & 99.15 \\ Segmenter(vit-s) [24] & 640 \(\times\) 640 & 98.80 & 99.23 \\ LDMNet & 640 \(\times\) 640 & 99.02 & 99.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy comparison of LDMNet with other advanced methods on Water Segmentation in the USVInland. LaRS: As shown in Table 4, in order to fully utilize the information retention capability of the Deep Dual-branch Network for high-resolution images, the images on the LaRS are cropped to 1024 \(\times\) 1024. The experimental results show that networks based on the Mamba architecture generally perform well, with the highest mIoU achieved by CM-UNet at 95.4%, and other methods also exceed 94.2% mIoU, demonstrating the advanced nature of Mamba in handling long-sequence data. It is worth noting that CMU-Net also uses CNN attention modules in conjunction with Mamba blocks, which confirms the compatibility and effectiveness of CNN and Mamba in long-sequence modeling. The MLLA based on Transformer also achieves a score of 95.3%, leveraging the advantages of the Mamba architecture to improve the shortcomings of linear attention, showing great potential, but its accuracy is still 0.3% mIoU lower than LDMNet. Methods solely based on CNN networks, except for SFNet, do not show competitiveness, which is related to the lack of global information modeling capability in CNN networks, which is also one of the reasons we tried to integrate Mamba into global modeling. Figure 6 demonstrates LDMNet's strong capability in detail and edge processing. We also analyzed the accuracy of various prediction results of LDMNet and baseline methods on the LaRS, as shown in Figure 7. It can be seen that the segmentation accuracy of all methods tends to stabilize for the \"water\"  
Figure 4: Visualization of the inference of LDMNet compared to TransNeXt [53] and VM-UNet V2 [15] on the MaStr1325 for close-up targets. We have marked the detailed differences with red boxes. Figure 5: Visualization of the inference of LDMNet compared to TransNeXt and VM-UNet V2 on the MaStr1325 for distant targets. We have marked the detailed differences with red boxes. and \"sky\" categories, while the segmentation accuracy of the \"obstacles and environment\" category determines whether the overall segmentation accuracy can be improved. Water Segmentation in the USVInland: To test LDMNet's performance in resisting light reflection on the water's surface, we trained LDMNet on the Water Segmentation in the USVInland and compared it with BiseNetV2, DDRNet-s, and Segmenter. The results are shown in Table 5. LDMNet significantly outperforms the benchmarks, with a mDice of 99.51% and a mIoU of 99.02%. Compared to Segmenter (vit-s) and DDRNet-s, the mDice is 0.28% and 0.36% higher, respectively, and the mIoU is 0.22% and 0.38% higher, respectively.