  The supervised training of deep networks for semantic segmentation requires a
huge amount of labeled real world data. To solve this issue, a commonly
exploited workaround is to use synthetic data for training, but deep networks
show a critical performance drop when analyzing data with slightly different
statistical properties with respect to the training set. In this work, we
propose a novel Unsupervised Domain Adaptation (UDA) strategy to address the
domain shift issue between real world and synthetic representations. An
adversarial model, based on the cycle consistency framework, performs the
mapping between the synthetic and real domain. The data is then fed to a
MobileNet-v2 architecture that performs the semantic segmentation task. An
additional couple of discriminators, working at the feature level of the
MobileNet-v2, allows to better align the features of the two domain
distributions and to further improve the performance. Finally, the consistency
of the semantic maps is exploited. After an initial supervised training on
synthetic data, the whole UDA architecture is trained end-to-end considering
all its components at once. Experimental results show how the proposed strategy
is able to obtain impressive performance in adapting a segmentation network
trained on synthetic data to real world scenarios. The usage of the lightweight
MobileNet-v2 architecture allows its deployment on devices with limited
computational resources as the ones employed in autonomous vehicles.
