Deep-Learning-Based Stair Detection Using 3D Point Cloud Data for Preventing Walking Accidents of the Visually Impaired

Haruka Matsumura, Chinthaka Premachandra

Research output: Contribution to journalArticlepeer-review

Abstract

Visually impaired individuals worldwide are at a risk of accidents while walking. In particular, falling from a raised place, such as stairs, can lead to serious injury. Therefore, we attempted to determine the best accident prevention method that can notify visually impaired individuals of the existence, height, and step information when they approach stairs. In this study, we have investigated stair detection through deep learning. First, the three-dimensional point cloud data generated from depth information are learned by deep learning. Stairs were detected using the results of deep learning. To apply the point cloud data for deep learning-based training, we proposed preprocessing stages to reduce the weight of the point cloud data. The accuracy of stair detection was 97.3%, which is the best performance compared to other conventional methods. Therefore, we confirmed the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)56249-56255
Number of pages7
JournalIEEE Access
Volume10
DOIs
Publication statusPublished - 2022

Keywords

  • 3D point cloud data
  • Deep-learning
  • Depth sensor
  • PointNet
  • Visually impaired support systems

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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