# A Benchmark for Water Surface Jet Segmentation with MobileHDC Method
## 5 Experiments
### Comparison  
In this subsection, we conduct extensive comparative experiments between our method and state-of-the-art CNN-based approaches, such as PIDNet and DSNet, as well as transformer-based methods, like SETR. The benchmark results on the Libary and SegQinhu datasets include accuracy metrics: accuracy, recall, F1 score, and mean intersection over union (mIoU); continuity metrics: the number of connected components and the proportion of pixels in connected components; and the efficiency evaluation metric: inference time. Accuracy. Overall, we observed that several mainstream semantic segmentation methods do not generalize well to jet segmentation, indicating that our two datasets pose new challenges for this task. For instance, models with purely transformer-based architectures, such as SETR, emphasize long-range dependencies and focus more on global information, often neglecting local details when segmenting jet trajectories, which leads to suboptimal performance. Conversely, CNN-based networks are more adept at capturing local features, but they exhibit certain limitations in acquiring global context. Although the UNet [37] model achieved a reasonable performance, its relatively complex architecture and high computational complexity may impact efficiency in practical applications. Consequently, we enhanced our model by introducing dilated convolutions into a lightweight MobileNetV2 backbone to expand the receptive field and improve the model's ability to learn global features, while also incorporating a decoder to fuse multi-scale features. Specifically, compared to the baseline, our model achieves improvements of 9.78% and 6.86% on the Libary \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Atrous Rates**} & \multicolumn{3}{c}{**Libary**} & \multicolumn{3}{c}{**SegQinhu**} \\ \cline{2-7}  & **Precision (\%)** & **Recall (\%)** & **mIoU (\%)** & **Precision (\%)** & **Recall (\%)** & **mIoU (\%)** \\ \hline None & 86.52 & 88.84 & 80.18 & 89.51 & 87.00 & 80.93 \\ (2, 3, 4, 5) & 79.73 & 95.63 & 78.12 & 96.83 & 82.37 & 81.01 \\ (2, 3, 4, 7) & 84.63 & 95.42 & 82.37 & 96.18 & 85.17 & 83.23 \\ (2, 4, 4, 5) & 86.48 & 92.13 & 82.10 & 98.16 & 83.39 & 82.56 \\ (2, 4, 4, 7) & 85.33 & 96.50 & 83.55 & 92.07 & 90.02 & 84.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study on the design of hybrid dilated convolution layers with different atrous rates. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Decoder**} & \multicolumn{2}{c}{**Libary**} & \multicolumn{3}{c}{**SegQinhu**} \\ \cline{2-7}  & **Precision (\%)** & **Recall (\%)** & **mIoU (\%)** & **Precision (\%)** & **Recall (\%)** & **mIoU (\%)** \\ \hline ✗ & 84.14 & 95.99 & 82.22 & 93.62 & 87.20 & 83.55 \\ ✓ & 85.33 & 96.50 & 83.55 & 92.07 & 90.02 & 84.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation study on the design of decoder module. and SegQinhu datasets, respectively, and outperforms current state-of-the-art methods DSNet and PIDNet in terms of mean intersection over union (mIoU), with gains of 1.28% and 0.67% on the Library dataset and 2.93% and 5.01% on the SegQinhu dataset. Continuity. Tables 6 and 7 present comparative results of different models regarding the number of connected components and the proportion of pixels in connected components. Our model exhibits fewer connected components and a higher proportion of pixels in connected components compared to most models, indicating a more continuous and complete segmentation result. Comprehensive consideration of these two metrics reveals that, on the Library dataset, the SeaFormer and DSNet models exhibit strong competitiveness, with the number of connected components being slightly lower than our model. On the SegQinhu dataset, while our model does not achieve the lowest number of connected components, its higher proportion of pixels in connected components indicates its capability to generate larger contiguous regions, resulting in more complete and concentrated segmentation outcomes. Inference time.