## 4 Experiments
### Comparison with Other Methods  
**Comparison on Dark Zurich-test**: In this paper, we first compare the proposed DLA-Net with several state-of-the-art methods on Dark Zurich-test [5], including CPSL [50], ProCA [51], and DiGA [52], as well as some other domain adaptation methods [6, 22, 53]. The performance results are summarized in Table 2.  
In Table 2, CPSL, ProCA, and DiGA are shown to have utilized the same baseline RefineNet, while the other methods employed DeepLab v3+. Additionally, all methods utilized ResNet-101 as the backbone [46], and the experimental dataset was Dark Zurich-test. DLA-Net with DeepLab-v3+, RefineNet, or PSPNet achieved superior or equivalent performance compared to existing methods on this dataset. It attained an overall improvement in mIoU of 2.2% compared to the highest score obtained by existing methods (DiGA). Furthermore, the DLA-Net proposed in this paper excelled in various categories, such as roads, sidewalks, and sky. For example, in the sky category, DLA-Net outperformed ProCA and DiGA by 51.6 mIoU and 27.9 mIoU, respectively, demonstrating its ability to accurately segment these categories despite a large daytime-nighttime domain gap. Figure 4 provides the visualization results of the comparison experiments with ProCA [51] and DiGA [52], highlighting the superior performance of DLA-Net in the categories of sky, road, and sidewalk. **Comparison on Nighttime Driving:** We compared the proposed DLA-Net with some other baseline methods on Nighttime Driving test [10], and results are reported in Table 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Dark Zurich-Val** & **CityScapes-Val** \\ \hline RefineNet [47] & 14.46 & 64.50 \\ PSPNet [48] & 11.44 & 64.97 \\ DeepLab-v3+ [49] & 11.58 & 63.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The performance of three distinct semantic segmentation models on the validation sets of CityScapes and Dark Zurich. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c} \hline \hline
**Method** & **Model**  
It is important to note that the Nighttime Driving test dataset is not as finely labeled as the Dark Zurich test dataset as some elements like buildings and vegetation are not labeled. Of the 50 images in the Nighttime Driving test dataset, only two are labeled with the sky category. Despite the limited number of labeled categories and the small dataset size, the DLA-Net with DeepLab-v3+ still achieved the second-best performance (DiGA was the top performer) on this dataset. Figure 5 reports the visualization results of the ProCA [51] and DiGA [52] comparison experiments. This underscores that the DLA-Net proposed in this paper can produce superior results even when working with a small number of samples and labeled categories for segmentation tasks. **Comparison of decoupling body and edge segmentation module with other advanced segmentation methods:** This paper uses ResNet-101 as a backbone [46] on the CityScapes dataset [25] to compare the decoupling body and edge segmentation module with some state-of-the-art techniques. The experimental results are listed in Table 4.  
As shown in Table 4, the decoupling body and edge segmentation method proposed in this paper achieved the highest mIoU among all methods, reaching 83.1 mIoU. This demonstrates the effectiveness of the body generation branch and the edge preservation branch in segmentation. The body branch with \(L_{2}\) loss constraints obtained prominent body features, while the edge branch with \(L_{1}\) loss constraints captured clear edge features. The combination of these features resulted in an overall segmentation map that has been experimentally proven to yield better segmentation performance. Ablation experiments between different components are discussed in the next section.