## 4 Experiments  
### Experimental Settings  
To assess the performance of the proposed DLA-Net and its components, the Mean Intersection-to-Noise Ratio (mIoU), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) were employed as the evaluation metrics in the experiments and compared with the advanced methods. The mIoU is a widely used metric for evaluating the accuracy of pixel-level semantic segmentation models which calculates the ratio of intersection and union between predicted segmentation results and true labels. PSNR and the SSIM are commonly used metrics for evaluating image enhancement work. Both metrics compare the differences between the original and compressed/distorted images. PSNR measures image quality by comparing the Peak Signal-to-Noise Ratio, while the SSIM evaluates it in terms of structure, brightness, and contrast similarity. In addition, the following datasets were used for the training of all segmentation models and performance evaluation during the daytime-nighttime domain adaptive transfer process. **CityScapes**[25]: The CityScapes dataset comprises 5000 street view images with a resolution of \(2048\times 1024\) divided into 2975 training images, 500 validation images, and 1525 testing images. Each image is annotated at the pixel level with 19 categories. We used the CityScapes training images as the training data in the training phase and the comparative experimental dataset for the decoupled body and edge segmentation modules; **Dark Zurich**[5]: The Dark Zurich dataset comprises 2416 nighttime images, 2920 twilight images, and 3041 daytime images for training, all with a resolution of \(1920\times 1080\). The images in these three domains are roughly aligned using GPS localization of neighboring locations and panning/zooming operations in all directions. In this paper, 2416 nighttime images were utilized to train the network model (without utilizing twilight images). In addition to the above images used for training, Dark Zurich contains 201 annotated nighttime images, of which 50 were used for validation (Dark Zurich-val) and 151 for testing (Dark Zurich-test) and evaluation;  
**Nighttime Driving**[10]: In the experiments, we exclusively utilized the Nighttime Driving test set, which comprises 50 nighttime images with a resolution of \(1920\times 1080\). All images are pixel-level annotated using 19 cityscape categories;  
**SICE dataset**[43]: The Part 2 subset of the SICE dataset was utilized in this paper, which comprises of 229 multi-exposure sequences and the reference image corresponding to each sequence. In the experiments, only low-light images from the Part 2 subset were used. In this study, we implemented the proposed adversarial learning domain adaptive semantic segmentation network using PyTorch on a single NVIDIA 3060 GPU, and all networks were trained using the same settings. Following [44], we trained the networks using an SGD optimizer and set the SGD optimizer momentum to 0.9 and a decay of \(5\times 10^{-4}\). The base learning rate of the network was \(2.5\times 10^{-4}\), and then the learning rate was reduced using a polynomial learning rate strategy with a decay power of 0.9. The batch size was 2. We used an Adam optimizer [45] to train the discriminators with the \(\beta\) set to (0.9, 0.99). The learning rate of discriminators followed the same decay strategy as the generator. The total enhancement loss \(L_{en}\) incorporates weights \(\lambda_{1}\) and \(\lambda_{2}\), which are selected from the intervals \([0.1-1.0]\) and \([20-25]\), respectively. These values were chosen based on previous similar work and different loss characteristics. After experimentation on the validation set, \(\lambda_{1}\) and \(\lambda_{2}\) were set to 0.4 and 20, respectively. In \(L_{de}\), the weight of \(L_{edge}\) is determined by \(\lambda_{3}\). This hyperparameter is set because \(L_{body}\) and \(L_{edge}\) are complementary, with fewer pixels in the edge part relative to the body part.