  Semantic segmentation plays an important role in intelligent vehicles,
providing pixel-level semantic information about the environment. However, the
labeling budget is expensive and time-consuming when semantic segmentation
model is applied to new driving scenarios. To reduce the costs, semi-supervised
semantic segmentation methods have been proposed to leverage large quantities
of unlabeled images. Despite this, their performance still falls short of the
accuracy required for practical applications, which is typically achieved by
supervised learning. A significant shortcoming is that they typically select
unlabeled images for annotation randomly, neglecting the assessment of sample
value for model training. In this paper, we propose a novel semi-supervised
active domain adaptation (SS-ADA) framework for semantic segmentation that
employs an image-level acquisition strategy. SS-ADA integrates active learning
into semi-supervised semantic segmentation to achieve the accuracy of
supervised learning with a limited amount of labeled data from the target
domain. Additionally, we design an IoU-based class weighting strategy to
alleviate the class imbalance problem using annotations from active learning.
We conducted extensive experiments on synthetic-to-real and real-to-real domain
adaptation settings. The results demonstrate the effectiveness of our method.
SS-ADA can achieve or even surpass the accuracy of its supervised learning
counterpart with only 25% of the target labeled data when using a real-time
segmentation model. The code for SS-ADA is available at
https://github.com/ywher/SS-ADA.
