  We present OffRoadTranSeg, the first end-to-end framework for semi-supervised
segmentation in unstructured outdoor environment using transformers and
automatic data selection for labelling. The offroad segmentation is a scene
understanding approach that is widely used in autonomous driving. The popular
offroad segmentation method is to use fully connected convolution layers and
large labelled data, however, due to class imbalance, there will be several
mismatches and also some classes may not be detected. Our approach is to do the
task of offroad segmentation in a semi-supervised manner. The aim is to provide
a model where self supervised vision transformer is used to fine-tune offroad
datasets with self-supervised data collection for labelling using depth
estimation. The proposed method is validated on RELLIS-3D and RUGD offroad
datasets. The experiments show that OffRoadTranSeg outperformed other state of
the art models, and also solves the RELLIS-3D class imbalance problem.
