  Currently, the visually impaired rely on either a sighted human, guide dog,
or white cane to safely navigate. However, the training of guide dogs is
extremely expensive, and canes cannot provide essential information regarding
the color of traffic lights and direction of crosswalks. In this paper, we
propose a deep learning based solution that provides information regarding the
traffic light mode and the position of the zebra crossing. Previous solutions
that utilize machine learning only provide one piece of information and are
mostly binary: only detecting red or green lights. The proposed convolutional
neural network, LYTNet, is designed for comprehensiveness, accuracy, and
computational efficiency. LYTNet delivers both of the two most important pieces
of information for the visually impaired to cross the road. We provide five
classes of pedestrian traffic lights rather than the commonly seen three or
four, and a direction vector representing the midline of the zebra crossing
that is converted from the 2D image plane to real-world positions. We created
our own dataset of pedestrian traffic lights containing over 5000 photos taken
at hundreds of intersections in Shanghai. The experiments carried out achieve a
classification accuracy of 94%, average angle error of 6.35 degrees, with a
frame rate of 20 frames per second when testing the network on an iPhone 7 with
additional post-processing steps.
