An Adaptive Semantic Segmentation Network for Adversarial Learning Domain Based on Low-Light Enhancement and Decoupled Generation

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.