  Staircases are some of the most common building structures in urban
environments. Stair detection is an important task for various applications,
including the environmental perception of exoskeleton robots, humanoid robots,
and rescue robots and the navigation of visually impaired people. Most existing
stair detection algorithms have difficulty dealing with the diversity of stair
structure materials, extreme light and serious occlusion. Inspired by human
perception, we propose an end-to-end method based on deep learning.
Specifically, we treat the process of stair line detection as a multitask
involving coarse-grained semantic segmentation and object detection. The input
images are divided into cells, and a simple neural network is used to judge
whether each cell contains stair lines. For cells containing stair lines, the
locations of the stair lines relative to each cell are regressed. Extensive
experiments on our dataset show that our method can achieve high performance in
terms of both speed and accuracy. A lightweight version can even achieve 300+
frames per second with the same resolution. Our code and dataset will be soon
available at GitHub.
