SDDA: Unsupervised Style and Distribution Domain Adaptation Method for Nighttime Semantic Segmentation

LEI Xiaochun; WU Weilin; JIANG Zetao; ZHU Wencai; LIU Yingjian; CHEN Dongmei; WU Siqi

doi:10.12263/DZXB.20251221

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SDDA: Unsupervised Style and Distribution Domain Adaptation Method for Nighttime Semantic Segmentation

PAPERS | 更新时间：2026-06-22

- SDDA: Unsupervised Style and Distribution Domain Adaptation Method for Nighttime Semantic Segmentation
- ACTA ELECTRONICA SINICA Vol. 54, Issue 1, Pages: 433-450(2026)
- 作者机构：
  
  1.桂林电子科技大学计算机与信息安全学院，广西桂林 541004
  2.广西图像图形与智能处理重点实验室，广西桂林 541004
  3.西北工业大学计算机学院，陕西西安 710072
- 作者简介：
- 基金信息：
  
  National Nature Science Foundation of China(62473105);Natural Science Foundation of Guangxi(2025JJA170088);Guangxi Key Laboratory of Image and Graphic Intelligent Processing(GIIP2305);Innovation Project of GUET Graduate Education(2025YCXS051;2024YCXS035)
- DOI：10.12263/DZXB.20251221
  CLC： TP391;
- Received：12 January 2026，
  
  Accepted：19 January 2026，
  
  Published：25 January 2026
- 稿件说明：
移动端阅览
雷晓春, 吴炜林, 江泽涛, 等. SDDA：无监督的风格和分布域适应夜间语义分割方法[J]. 电子学报, 2026, 54(01): 433-450.

LEI Xiaochun, WU Weilin, JIANG Zetao, et al. SDDA: Unsupervised Style and Distribution Domain Adaptation Method for Nighttime Semantic Segmentation[J]. Acta Electronica Sinica, 2026, 54(01): 433-450.
雷晓春, 吴炜林, 江泽涛, 等. SDDA：无监督的风格和分布域适应夜间语义分割方法[J]. 电子学报, 2026, 54(01): 433-450. DOI：10.12263/DZXB.20251221

LEI Xiaochun, WU Weilin, JIANG Zetao, et al. SDDA: Unsupervised Style and Distribution Domain Adaptation Method for Nighttime Semantic Segmentation[J]. Acta Electronica Sinica, 2026, 54(01): 433-450. DOI：10.12263/DZXB.20251221

摘要

语义分割在自动驾驶、医工交叉和安防监控等多种实际应用中发挥着重要作用，但夜间语义分割仍然是未解决的一道难题。由于夜间光照不足，获取的图像细节模糊不清，导致数据集标注困难，因而人们首选探索无监督域适应夜间语义分割方法。虽然取得了一些进展，但仍然存在数据集跨域幅度太大难以直接进行域适应的问题，导致夜间场景的语义分割效果不理想。针对这个问题，本文提出了一种风格和分布域适应（Style and Distribution Domain Adaptation，SDDA）的无监督夜间语义分割方法，将夜间语义分割任务的域适应分为风格域适应和分布域适应，以此降低夜间分割任务的难度。将性能更优秀的Mamba架构模型引入无监督域适应夜间语义分割任务中，探索该架构模型在夜间语义分割任务的优势，以提升夜间分割任务的精度。提出了一个语义对齐图像翻译（Semantic Pairing GAN，SPG）模块，通过语义信息将非配对翻译和粗配对翻译相结合，以此将分割任务与SPG翻译模块进行语义关联，促进翻译内容更加适合分割任务，且不独立于分割任务。SPG模块先将源域白天图像翻译成夜间图像，然后分割模型用翻译后的图像进行训练，这样分割模型就能学习到风格域信息以减少风格域差异。提出了一种语义域混合（Semantic Domain Mixing，SDM）策略，利用语义信息将SPG翻译的动态物体提取并移动到目标域夜间静态物体图像的合理位置，重新组合成新的图像。分割模型利用这种风格域差异小的图像进行训练，可以更容易从分布域角度进行域适应，从而缩小分布域差距。通过风格域适应和分布域适应相结合，使模型从两种不同角度分别缩小域差异，整体上实现夜间分割任务的域适应，从而缓解现有数据集跨域幅度太大，难以直接域适应的问题。实验结果表明，本文的方法在Dark Zurich、ACDC Night和Nighttime Driving三个数据集上的mIoU指标分别取得60.0%、59.8%、59.1%，比现有最好的方法分别提高0.9%，0.4%和1.6%，对夜间复杂实际场景图像目标能进行精准的分割预测。

Abstract

Semantic segmentation plays an important role in a variety of practical applications such as autonomous driving

doctor-worker intersection

and security monitoring. However

nighttime semantic segmentation is still an unsolved problem. Due to insufficient illumination at night

the details of the acquired image are unclear

which leads to the difficulty of dataset annotation. Therefore

unsupervised domain adaptation methods for nighttime semantic segmentation are preferred. As a result

the semantic segmentation effect of nighttime scenes is not ideal. To solve this problem

this paper proposes an unsupervised SDDA (Style and Distribution Domain Adaptation) method for nighttime semantic segmentation. The domain adaptation of nighttime semantic segmentation task is divided into style domain adaptation and distribution domain adaptation. In this way

the difficulty of the nighttime segmentation task is reduced. The Mamba architecture model with better performance is introduced into the unsupervised domain to adapt to the nighttime semantic segmentation task

and the advantages of this architecture model in the nighttime semantic segmentation task are explored to improve the accuracy of the nighttime segmentation task. This paper proposes a SPG (Semantic Pairing GAN) module

which combines the unpaired translation and rough paired translation through semantic information

so as to semantically associate the segmentation task with the SPG translation module

so as to promote the translation content to be more suitable for the segmentation task and not independent of the segmentation task. The SPG module translates the day images of the source domain into night images

and then the segmentation model is trained with the translated images

so that the segmentation model can learn the style domain information to reduce the style domain differences. This paper proposes a SDM (Semantic Domain Mixing) strategy

which uses semantic information to extract and move the dynamic objects translated by SPG to the reasonable position of the static object image at night in the target domain

and recombines them into a new image. The segmentation model is trained by using the images with small style domain differences

which makes it easier to perform domain adaptation from the perspective of distribution domain

so as to narrow the distribution domain gap. Through the combination of style domain adaptation and distribution domain adaptation

the model reduces the domain differences from two different perspectives

and realizes the domain adaptation of night segmentation tasks as a whole

so as to alleviate the problem that the existing data sets have too large cross-domain range and are difficult to directly adapt to the domain. The experimental results show that the mIoU index of the proposed method on Dark Zurich

ACDC Night and Nighttime Driving datasets achieves 60.0%

59.8% and 59.1%

respectively

which is 0.9%

0.4% and 1.6% higher than the best existing method. It can accurately segment and predict the image target of complex actual scene at night.

关键词

Keywords

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