###### Abstract  
Nighttime semantic segmentation due to issues such as low contrast, fuzzy imaging, and low-quality annotation results in significant degradation of masks. In this paper, we introduce a domain adaptive approach for nighttime semantic segmentation that overcomes the reliance on low-light image annotations to transfer the source domain model to the target domain. On the front end, a low-light image enhancement sub-network combining lightweight deep learning with mapping curve iteration is adopted to enhance nighttime foreground contrast. In the segmentation network, the body generation and edge preservation branches are implemented to generate consistent representations within the same semantic region. Additionally, a pixel weighting strategy is embedded to increase the prediction accuracy for small targets. During the training, a discriminator is implemented to distinguish features between the source and target domains, thereby guiding the segmentation network for adversarial transfer learning. The proposed approach's effectiveness is verified through testing on Dark Zurich, Nighttime Driving, and CityScapes, including evaluations of mIoU, PSNR, and SSIM. They confirm that our approach surpasses existing baselines in segmentation scenarios. domain adaptation; nighttime semantic segmentation; adversarial learning; low-light enhancement +
Footnote †: journal: Medical  
high-quality pixel-level annotation on this part of the image. Consequently, a sufficient quantity of accurate segmentation instances is the basis for realizing efficient learning of segmentation models. To tackle this, current schemes such as domain adaptation [6; 7], synthetic datasets [8], and style transfer [9] are commonly employed. Due to the low brightness and contrast of nighttime images, this paper transfers daytime images to nighttime images by domain adaptation. However, large differences in scene feature distributions and foreground types between the source and target domains, primarily concerning light intensity, can often lead to the distortion of crucial spatial semantic details during domain transfer. In view of this, some studies have proposed to establish domain transfer from the daytime domain to the nighttime domain using an intermediate domain as a smooth transition, such as the twilight domain. In [5; 10; 11], the twilight domain serves as a bridge, allowing the model trained in the daytime domain to adapt progressively to the nighttime domain by extracting features from twilight images and performing transfer alignment learning. In [12], a model adaptation method based on course learning is proposed, which adapts the model to light changes and noise in nighttime scenes by gradually increasing the complexity of the nighttime images. Building [12], research conducted in [13] utilizes feature maps to provide prior knowledge about nighttime scenes, aiding the model in understanding objects and structures and guiding adaptive training. In [14], an encoder-decoder structure for semantic segmentation of nighttime images is introduced which uses a domain map approach for mapping synthetic to real data. In [15; 16; 17], a generator network is trained using adversarial learning to translate daytime images to nighttime images. Subsequently, the feature extractor of the generator network is adopted for the semantic segmentation network to extract transform-based regularized features from nighttime images. Furthermore, to enhance the generalization performance of the model, research in [18] employs Adversarial Generative Networks (GANs) to translate daytime images to nighttime images, and random transformations are then applied to those images, followed by joint training using the adversarial and semantic segmentation loss functions. Among the above-mentioned methods, refs. [5; 10; 11; 12; 13] leverage intermediate domains to create a smooth transition, thereby improving model generalization and potentially reducing dataset labeling costs.