## 4 Experiment
### Speed and Accuracy Comparisons  
During inference, the batch size is set to 1, using cuda 11.1, CUDNN 8004, and Pytorch 1.9.1 + cu111. Through fair comparison with other benchmarks on datasets such as MaStr1325, LaRS, Water Segmentation in the USVInland, and Cityscapes, the results are presented in Tables 3-6. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Type** & **GPU** & **Resolution** & **Params \(\downarrow\)** & **mIoU (\%) \(\uparrow\)** & **Speed (FPS) \(\uparrow\)** \\ \hline Deeplab V3+ [22] & CNN & V100 & \(512\times 512\) & - & 85.4 & 0.56 \\ SegNet [23] & CNN & V100 & \(512\times 512\) & - & 81.8 & 0.85 \\ WODNS [52] & CNN & V100 & \(512\times 384\) & 89.5 M & 91.3 & 43.2 \\ Fast SCNN [25] & CNN & 3070 Ti & \(512\times 512\) & 1.36 M & 93.5 & 67.5 \\ DDRNet-s [8] & CNN & 3070 Ti & \(512\times 512\) & 17.05 M & 94.5 & 79 \\ \hline Segmenter(vit-s) [24] & Transformer & 3070 Ti & \(512\times 512\) & - & 94.8 & 53.4 \\ TransNeXt- [53] & Transformer & 4090 Ti & \(512\times 512\) & 28.2 M & 95.4 & 10.3 \\ UNetformer(R18) [20] & Transformer & 3070 Ti & \(512\times 512\) & 11.69 M & 94.2 & 5.6 \\ \hline VMamba-s [13] & Mamba & 3070 Ti & \(512\times 512\) & 70 M & 93.6 & 52 \\ VM-UNet [14] & Mamba & 3070 Ti & \(512\times 512\) & 34.62 M & 93.4 & 21.1 \\ VM-UNetV2 [15] & Mamba & 3070 Ti & \(512\times 512\) & 17.91 M & 94.8 & 32 \\ CM-UNet [16] & Mamba & 3070 Ti & \(512\times 512\) & 12.89 M & 93.7 & 8.5 \\ LDMNet & CNN\&Mamba & 3070 Ti & \(512\times 512\) & 12.53 M & 96.2 & 80 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy comparison of LDMNet with other advanced methods on MaStr1325. Among them, ”-” indicates a lack of relevant data. MaSTr1325: From Table 3, it can be observed that LDMNet achieves a good compromise between accuracy and real-time performance. On the MaSTr1325 test set, LDMNet reaches a mIoU of 96.2% at a speed of 80 FPS under the aforementioned hardware environment, which is 1.7% higher than DDRNet-s with a similar speed. Other methods, such as Segmenter and Fast SCNN, are outperformed by LDMNet on the test set with mIoU improvements of 1.4% and 2.7%, respectively, and LDMNet also operates faster than both of them. WODIS, proposed specifically for the detection of surface obstacles on maritime autonomous surface vehicles, achieves an mIoU of 91.3% at 43.2 FPS on the MaSTr1325 test set, but this result is 4.9% lower than our proposed method and 36.8% slower in terms of speed. In comparison with many models based on the Transformer architecture and Mamba architecture, LDMNet outperforms TransNeXt-t by 0.8 mIoU and improves over other methods by 1.4% to 2.8% mIoU. We visualize the inference results of LDMNet and other methods on MaSTr1325 in Figures 4 and 5. By comparing the segmentation effects of near and distant targets, it is evident that LDMNet can handle the edges of target objects more finely and demonstrates its strong capability in capturing details for elongated and small target edges. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Resolution** & **mIoU (\%)**\(\uparrow\) & **GFLOPs \(\downarrow\)** \\ \hline SFNet(DF1) [56] & 2048 \(\times\) 1024 & 74.5 & 24.7 \\ STDC2-Seg75 [55] & 1536 \(\times\) 768 & 76.8 & - \\ PP-LiteSeg-T2 [9] & 1536 \(\times\) 768 & 74.9 & - \\ HyperSeg-M [30] & 1024 \(\times\) 512 & 75.8 & 7.5 \\ PIDNet-S [7] & 2048 \(\times\) 1024 & 77.1 & 46.3 \\ DDRNet-s [8] & 2048 \(\times\) 1024 & 77.2 & 34.2 \\ LDMNet & 2048 \(\times\) 1024 & 80.7 & 32.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy comparison of LDMNet with other advanced methods on Cityscapes. Among them, ”-” indicates a lack of relevant data.