  Semantic segmentation using deep neural networks has been widely explored to
generate high-level contextual information for autonomous vehicles. To acquire
a complete $180^\circ$ semantic understanding of the forward surroundings, we
propose to stitch semantic images from multiple cameras with varying
orientations. However, previously trained semantic segmentation models showed
unacceptable performance after significant changes to the camera orientations
and the lighting conditions. To avoid time-consuming hand labeling, we explore
and evaluate the use of data augmentation techniques, specifically skew and
gamma correction, from a practical real-world standpoint to extend the existing
model and provide more robust performance. The presented experimental results
have shown significant improvements with varying illumination and camera
perspective changes.
