Blind sidewalk segmentation based on the lightweight semantic segmentation network

Abstract

A lightweight semantic segmentation network is proposed to solve the problem of real-time segmentation of blind sidewalk. On the basis of U-Net’s encode-decoding structure, the inverse residual block composed of deep separable convolution is used to replace the ordinary convolution for feature extraction, and the number of convolution layer channels is compressed. Experiments show that the blind segmentation method used can effectively overcome the disadvantages of traditional methods that are affected by the environment easily. Compared with U-Net, the parameter amount is reduced by 25.2 times, and the reasoning speed is increased by 3.6 times, while the loss of precision is just less than 2%, which basically meet the requirement of real-time inference.