## 4 Experiments
### Ablation Study  
To demonstrate the effectiveness of the different components of the proposed DLA-Net in this paper, several ablation experiments were conducted on several model variants. The results of the ablation experiments on different components are detailed below. **Ablation study on decoupling body and edge modules:** The effectiveness of the two branches in the decoupling body and edge segmentation network is illustrated in Table 6, where \(\rho\) and \(\delta\) denote the body generation branch and the edge preservation branch, respectively. The direct addition of the body generation and edge preservation branches in DeepLab-V3+ [49] improved the segmentation effect by 1.7%, implying that both branches are effective. After adding \(L_{body}\) and \(L_{edge}\), respectively, there were further improvements of 0.5% and 0.4% in performance, demonstrating that using \(L_{2}\) and \(L_{1}\) loss constraints can facilitate the model's learning of different features. Finally, when all losses were combined, the performance was further improved by 1.7%. This paper also investigated the necessity of the \(F_{detail}\) module, and its removal resulted in a decrease in segmentation performance of about 0.7%. The results of the ablation studies for each component of the decoupling body and edge segmentation module are shown in Table 7. After removing the average pooling layer and the encoder-decoder in the body generation branch, the model performance decreases by 1.5% and 1.0% accordingly. After removing the edge preservation branch, the model performance decreased by 0.4%. Therefore, removing the three modules individually leads to varying magnitudes of degradation in segmentation network performance. This indicates that average pooling and codecs help predict the body feature in the body generation branch and that average pooling improves the performance to a greater extent, while the entire edge preservation branch can also elevate the performance of the segmentation network. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \(L_{body}\) & \(L_{edge}\) & **mIoU** & \(\Delta\)**(\%)** \\ \hline DeepLab-v3+ [49] & & & 74.6 & - \\ +\(\rho\)\& \(\delta\) & - & - & 75.9 & 1.7\(\uparrow\) \\ \hline  & \(\checkmark\) & - & 76.3 & 0.5\(\uparrow\) \\  & - & \(\checkmark\) & 76.6 & 0.4\(\uparrow\) \\  & \(\checkmark\) & \(\checkmark\) & 77.6 & 1.7\(\uparrow\) \\ \hline w/o \(F_{detail}\) & \(\checkmark\) & \(\checkmark\) & 77.1 & 0.7\(\downarrow\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of decoupled body and edge segmentation module with other advanced segmentation methods. \(\checkmark\) indicates that \(L_{body}\) or \(L_{edge}\) was used. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **mIoU** & \(\Delta\)**(\%)** \\ \hline DeepLab-v3+ [49] +\(\rho\)\& \(\delta\) & 77.6 & - \\ \hline w/o \(\rho\) average pooling & 76.4 & \(1.5\)\(\downarrow\) \\ w/o \(\rho\) encoder–decoder & 76.8 & \(1.0\)\(\downarrow\) \\ w/o \(\delta\) & 77.2 & \(0.4\)\(\downarrow\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ablation study on effect of each component. **Ablation study for each loss in the low-light image enhancement sub-network:** The visualization results of the ablation experiments with different loss functions in the LIE-SubNet are presented in Figure 6. After removing the exposure control loss \(L_{cc}\), the image exhibited overexposure in areas with strong lighting, underscoring the effectiveness of exposure constraints in the network. The removal of the color consistency loss \(L_{cc}\) resulted in the overall image's severe color deviation. With the removal of the light smoothing loss \(L_{is}\), artifacts appeared between adjacent regions in the image. These experiments highlight the critical contributions of each loss function used in this paper in the LIE-SubNet. **Ablation study on different components of DLA-Net:** As shown in Table 8, AdaptSegNet [6] was used as the baseline and DLA-Net as the full model.