Rapid detection of blind roads and crosswalks by using a lightweight semantic segmentation network

Zhengcai Cao, Xiaowen Xu, Biao Hu, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Achieving the high accuracy of blind roads and crosswalks recognition is important for blind guiding equipment to help blind people sense the surrounding environment. A lightweight semantic segmentation network is proposed to quickly and accurately segment blind roads and crosswalks in a complex road environment. Specifically, a lightweight network with depthwise separable convolution as a component is used as a basic module to reduce the number of parameters of the model and increase the speed of semantic segmentation. In order to ensure the segmentation accuracy of the network, we use a densely connected atrous spatial pyramid pooling module to extract feature information of different angles and context feature modules to enhance the effectiveness of different levels of feature information fusion. To verify the effectiveness of the proposed method, we collect and produce a data set from a real environment, which contains two objects of blind roads and crosswalks1. Experimental results demonstrate that, compared to some state-of-the-art approaches, the proposed approach greatly improves the segmentation speed, while achieving better or similar accuracy, which shows that the proposed approach provides a better basis for the application of devices for guiding the blind.

Original languageEnglish (US)
Pages (from-to)6188-6197
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Volume22
Issue number10
DOIs
StatePublished - Oct 1 2021

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

Keywords

  • Blind roads and crosswalks
  • Deep convolutional network
  • Lightweight structure
  • Semantic segmentation

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