  Off-road image semantic segmentation is challenging due to the presence of
uneven terrains, unstructured class boundaries, irregular features and strong
textures. These aspects affect the perception of the vehicle from which the
information is used for path planning. Current off-road datasets exhibit
difficulties like class imbalance and understanding of varying environmental
topography. To overcome these issues we propose a framework for off-road
semantic segmentation called as OFFSEG that involves (i) a pooled class
semantic segmentation with four classes (sky, traversable region,
non-traversable region and obstacle) using state-of-the-art deep learning
architectures (ii) a colour segmentation methodology to segment out specific
sub-classes (grass, puddle, dirt, gravel, etc.) from the traversable region for
better scene understanding. The evaluation of the framework is carried out on
two off-road driving datasets, namely, RELLIS-3D and RUGD. We have also tested
proposed framework in IISERB campus frames. The results show that OFFSEG
achieves good performance and also provides detailed information on the
traversable region.
