Train Here, Deploy There: Robust Segmentation in Unseen Domains

Semantic Segmentation methods play a key role in today's Autonomous Driving research, since they provide a global understanding of the traffic scene for upper-level tasks like navigation. However, main research efforts are being put on enlarging deep architectures to achieve marginal accuracy boosts in existing datasets, forgetting that these algorithms must be deployed in a real vehicle with images that were not seen during training. On the other hand, achieving robustness in any domain is not an easy task, since deep networks are prone to overfitting even with thousands of training images. In this paper, we study in a systematic way what is the gap between the concepts of 'accuracy' and 'robustness'. A comprehensive set of experiments demonstrates the relevance of using data augmentation to yield models that can produce robust semantic segmentation outputs in any domain. Our results suggest that the existing domain gap can be significantly reduced when appropriate augmentation techniques regarding geometry (position and shape) and texture (color and illumination) are applied. In addition, the proposed training process results in better calibrated models, which is of special relevance to assess the robustness of current systems.