KrNet: A kinetic real-time convolutional neural network for navigational assistance

Abstract

Over the past years, convolutional neural networks (CNN) have not only demonstrated impressive capabilities in computer vision but also created new possibilities of providing navigational assistance for people with visually impairment. In addition to obstacle avoidance and mobile localization, it is helpful for visually impaired people to perceive kinetic information of the surrounding. Road barrier, as a specific obstacle as well as a sign of entrance or exit, is an underlying hazard ubiquitously in daily environments. To address the road barrier recognition, this paper proposes a novel convolutional neural network named KrNet, which is able to execute scene classification on mobile devices in real time. The architecture of KrNet not only features depthwise separable convolution and channel shuffle operation to reduce computational cost and latency, but also takes advantage of Inception modules to maintain accuracy. Experimental results are presented to demonstrate qualified performance for the meaningful and useful applications of navigational assistance within residential and working area.