  Blind and visually challenged face multiple issues with navigating the world
independently. Some of these challenges include finding the shortest path to a
destination and detecting obstacles from a distance. To tackle this issue, this
paper proposes ViT Cane, which leverages a vision transformer model in order to
detect obstacles in real-time. Our entire system consists of a Pi Camera Module
v2, Raspberry Pi 4B with 8GB Ram and 4 motors. Based on tactile input using the
4 motors, the obstacle detection model is highly efficient in helping visually
impaired navigate unknown terrain and is designed to be easily reproduced. The
paper discusses the utility of a Visual Transformer model in comparison to
other CNN based models for this specific application. Through rigorous testing,
the proposed obstacle detection model has achieved higher performance on the
Common Object in Context (COCO) data set than its CNN counterpart.
Comprehensive field tests were conducted to verify the effectiveness of our
system for holistic indoor understanding and obstacle avoidance.
