## 4 Experiments
### Comparison with Other Methods
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **mIoU** \\ \hline RefineNet-CityScapes [47] & 32.75 \\ PSPNet-CityScapes [48] & 25.44 \\ DeepLab-v3+-CityScapes [49] & 27.65 \\ \hline AdapSegNet-CityScapes\(\rightarrow\) DZ-night [6] & 34.5 \\ ADVENT-CityScapes\(\rightarrow\)DZ-night [53] & 34.7 \\ BDL-CityScapes\(\rightarrow\)DZ-night [22] & 34.7 \\ CPSL [50] & 38.2 \\ ProCA [51] & 46.7 \\ DiGA [52] & **49.9** \\ \hline DLA-Net (RefineNet) & 43.82 \\ DLA-Net (PSPNet) & 44.59 \\ DLA-Net (DeepLab-v3+) & 47.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results of the proposed DLA-Net and some baseline methods on Nighttime Driving test. Bold font indicates the **best**, and underlining indicates the _second-best_. Figure 4: Visualization results of DLA-Net and some other baseline methods on Dark Zurich-val. **Comparison of low-light image enhancement sub-network with other methods:** In this paper, reference image quality assessment metrics PSNR and SSIM were used to quantitatively compare the performance of different methods on the SICE Part 2 test set [43]. Higher values of SSIM and PSNR indicate that the enhanced image is closer to the ground truth in terms of structural properties and pixel-level image content, respectively. The experimental results are presented in Table 5.  
Table 5 reveals that, despite not using any paired or unpaired training data, the LIEBubNet proposed in this paper still achieved the best PSNR and second-best SSIM results (EnlightenGAN was the top performer). Combining the mapping curve and the depth network resulted in a 1.1% improvement compared to the second-best performance, and the mapping curve with multiple iterations made the overall pixels of the low-light images more uniform (see Figure 5). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Backbone** & **mIoU** \\ \hline DFN [54] & ResNet-101 & 79.3 \\ PSANet [55] & ResNet-101 & 80.1 \\ DenseASPP [56] & DenseNet-161 & 80.6 \\ DANet [57] & ResNet-101 & 81.5 \\ CCNet [58] & ResNet-101 & 81.4 \\ BAFNet [59] & ResNet-101 & 81.4 \\ ACFNet [60] & ResNet-101 & 81.9 \\ GFFnet [61] & ResNet-101 & 82.3 \\ X. Li et al. [62] & ResNet-101 & 82.8 \\ \hline Ours & ResNet-101 & **83.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of decoupled body and edge segmentation module with other advanced segmentation methods. Bold font indicates the **best**, and underlining indicates the second-best. Figure 5: Visualization results of ablation experiments with different loss functions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** \\ \hline MBLLEN [34] & 14.78 & 0.534 \\ RetiexNet [63] & 15.56 & 0.525 \\ RUAS [64] & 16.40 & 0.500 \\ ZeroDCE [37] & 14.86 & 0.559 \\ SCI [65] & 14.78 & 0.522 \\ EnlightenGAN [42] & 17.48 & **0.651** \\ \hline Ours & **18.10** & 0.638 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of low-light image enhancement sub-networks with other methods. Bold font indicates the **best**, and underlining indicates the second-best. In this subsection, we present comparative experiments of the overall method using the Dark Zurich dataset and the Nighttime Driving dataset. The experimental results validate the excellent performance of the proposed method. However, it should be noted that the Dark Zurich dataset mostly contains unobstructed image foregrounds, and there exists a one-to-one correspondence between the daytime and nighttime images. As a result, DLA-Net is able to perform optimally and obtain excellent results. When there is occlusion in the foreground of an image, LIE-SubNet and decoupled subject and segmentation networks may not be effective in enhancing the image and performing decoupled segmentation.