Domain Adaptation Frameworks for Road Scene Segmentation in Unseen Environments

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The objective of this research is to develop domain generalization frameworks for the task of semantic segmentation of road scene images using deep learning. We surveyed multiple state-of-the-art existing methods and identified underline hyper-parameters that led to the overfitting of models on the source domains which led to poor generalization. We also identified various techniques that are very useful for domain generalization like mixing styles in feature space and the good range for color jittering paramters like contrast, brightness, saturation, and hue. We observed that our findings are consistent across all the benchmark target domains that we tested. Based on these findings, we have developed an ensemble model for domain generalization which achieves very good performance.