# A Benchmark for Water Surface Jet Segmentation with MobileHDC Method
## 5 Experiments
### Comparison
Under the same testing conditions, including input image resolution, batch size, and hardware environment, we conducted a fair comparison of the inference times across different models. As shown in Table 8, our model demonstrates a significant advantage in inference speed over other models, reflecting its high computational efficiency. Although slightly slower than the baseline in terms of inference time, our model exhibits \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Backbone**} & \multicolumn{5}{c}{**Accuracy**} & \multicolumn{5}{c}{**Continuity**} \\ \cline{5-10}  & & & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** & **mIoU (\%)** & \(\mathbf{D_{\text{H}}}\) (_pixel_) & \(\mathbf{N_{\text{c}}}\) & \(\mathbf{R_{\text{c}}}\) **(\%)** \\ \hline MobileNetv2 [11] & CVPR18 & MobileNet2 & 84.27 & 80.41 & 82.22 & 73.77 & 81.86 & 6.78 & 0.11 \\ UNet [37] & MICCA15 & - & (84.34) & (77.92) & (00.03) & (83.32) & 19.91 & 1.84 & 1.07 \\ MobileNetv3 [38] & ICCV19 & V3-Small & 70.40 & 72.59 & 71.44 & 63.64 & 34.08 & 3.04 & 0.21 \\ SETR [39] & CVPR21 & ViT-Base & 62.58 & 76.46 & 67.05 & 60.28 & 114.62 & 6.03 & 0.14 \\ SegFormer [40] & NeurIPS21 & MiT-B0 & 82.71 & (98.68) & 89.13 & 82.14 & 31.98 & 1.82 & 1.35 \\ SegNext [41] & NeurIPS22 & MSCAN-T & 80.93 & 97.13 & 87.35 & 79.80 & 18.66 & 2.12 & 0.56 \\ SeaFormer [24] & ICLR23 & SeaFormer-B & (84.53) & (77.81) & (00.09) & 82.47 & (15.98) & (1.16) & (1.22) \\ PIDNet-S [42] & CVPR23 & - & 83.93 & 97.75 & 89.67 & (82.88) & 16.72 & (1.20) & 1.26 \\ DSNet- & TCSV124 & - & 84.12 & 97.20 & 89.70 & 82.27 & (16.46) & 1.31 & (1.40) \\ \hline Ours & - & MobileHDC & (85.33) & 96.50 & (0.15) & (83.55) & (15.76) & (1.28) & (1.30) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparisons with other state-of-the-art semantic segmentation methods on Libary test set. Gold, silver, and bronze indicate the top three scores in each metric. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Backbone**} & \multicolumn{5}{c}{**Accuracy**} & \multicolumn{5}{c}{**Continuity**} \\ \cline{3-10}  & & & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** & **mIoU (\%)** & \(\mathbf{D_{\text{H}}}\) (_pixel_) & \(\mathbf{N_{\text{c}}}\) & \(\mathbf{R_{\text{c}}}\) **(\%)** \\ \hline MobileNetv2 [11] & CVPR18 & MobileNetv2 & 85.12 & (66.58) & 85.84 & 77.92 & 46.28 & 4.56 & 0.64 \\ UNet [37] & MICCA15 & - & 86.40 & (90.46) & (88.38) & 81.07 & 34.26 & 2.39 & 0.87 \\ MobileNetv3 [38] & ICCV19 & V3-Small & 92.99 & 72.96 & 79.93 & 71.36 & 48.22 & 3.04 & 0.13 \\ SETR [39] & CVPR21 & ViT-Base & 88.19 & 70.49 & 76.67 & 68.18 & 60.90 & 4.78 & 0.19 \\ SegFormer [40] & NeurIPS21 & MiT-B0 & (95.95) & 82.95 & (88.38) & 81.14 & 31.98 & (1.84) & 0.88 \\ SegNext [41] & NeurIPS22 & MSCAN-T & (98.98) & 78.15 & 85.75 & 77.82 & (20.12) & 2.36 & 0.69 \\ SeaFormer [24] & ICLR23 & SeaFormer-B & (97.67) & 84.22 & (99.84) & (83.11) & (18.16) & (1.78) & (1.89) \\ PIDNet-S [42] & CVPR23 & - & 90.39 & 84.69 & 87.33 & 79.77 & 22.31 & 2.12 & 0.35 \\ DSNet- & TCSV124 & - & 91.50 & 84.01 & 88.10 & (11.88) & (11.88) & 21.97 & 1.94 & (1.95) \\ head64 [21] & & & & & & & & & \\ \hline Ours & - & MobileHDC & 92.07 & (90.02) & (91.03) & (84.78) & (21.73) & (1.88) & (9.92) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparisons with other state-of-the-art semantic segmentation methods on SegQinhu test set. Gold, silver, and bronze indicate the top three scores in each metric. notable improvements in other evaluation metrics, indicating a well-balanced trade-off between accuracy and speed. Visualization. To further demonstrate the effectiveness of our method, we present a series of visualization examples from the Libary and SegQinhu datasets, as shown in Figures 8 and 9. These visualizations provide compelling insights into the segmentation performance of various models.