###### Abstract
Nonetheless, introducing intermediate domains may entail additional preprocessing and model training and fail to fully cover all variations from daytime to nighttime. On the other hand, refs. [14; 15; 16; 18] employ techniques such as style transfer and build synthetic datasets to address the difficulty of labeling nighttime (low-light) samples. While building synthetic datasets offers advantages, it also carries the risk of introducing bias and noise. Furthermore, these schemes focus only on the statistical representation of the overall image style in style transfer and thus are prone to the loss of spatial details. In addition, generating a transferred image with the same semantics as the original image, especially when dealing with a relatively large domain gap, remains a challenging aspect of image translation. To address these problems, this paper uses pairs of day and night images in similar scenes as target domains and tries to transfer the source domain generalized model to the scene-specific multi-target domain without introducing an intermediate domain, synthetic datasets, or style migrations so as to improve the segmentation quality by joint adversarial learning and multi-domain co-training. Based on this, this paper proposes an adaptive semantic segmentation network for the adversarial learning domain based on low-light enhancement and decoupled generation (DLA-Net). At the front end of the model, a lightweight low-light image enhancement network (LIE-SubNet) is embedded to elevate foreground contrast in nighttime images and accomplish spatial feature alignment across different illuminance datasets. Existing segmentation models typically treat the foreground target as a unified entity; however, foreground boundary regions usually contain richer spatial details with higher-frequency feature information, whereas non-boundary regions exhibit fewer spatial details featured by low-frequency distributions. Inspired by [6], this paper leverages a generative network capable of decoupling the foreground body and edge to predict segmentation masks and uses two discriminators for adversarial training between the source and target domains. Additionally, a small pixel reweighting strategy [19] is implemented to process the input images and reduce prediction uncertainty, thus improving the segmentation accuracy for small targets. In our experiments, the Dark Zurich dataset [5] is employed, which contains pairs of daytime and nighttime images based on rough GPS positional alignment. Through extensive testing on the Dark Zurich, CityScapes, and Nighttime Driving [10] datasets, the proposed method is verified to demonstrate improved performance in low-light nighttime semantic segmentation. The primary contributions of our work are summarized as follows:  
* In this paper, a multi-domain model joint training network for semantic segmentation, DLA-Net, is introduced which transfers the source domain to the multi-target domain of a specific scene without requiring an intermediate domain. It accomplishes joint adversarial training of the multi-domain model, supported by the low-light image enhancement sub-network, on the multi-target domain;
* The low-light image enhancement sub-network, LIE-SubNet, which combines deep learning and mapping curve iteration, is proposed to enhance pixel contrast and spatial feature alignment of nighttime images. In the segmentation network, a generative network capable of decoupling subjects and edges is utilized to guide segmentation prediction via exploiting the adversarial loss in the daytime and nighttime domains;
* To effectively utilize both low-frequency and high-frequency information of foreground targets, the segmentation mask is decoupled into the body generation branch and the edge preservation branch. These branches can focus on different attributes of the regional features during training. The resulting masks are then composited and reconstructed to achieve a complete semantic segmentation mask capable of retaining the details while removing the void noise.