An Embedded Machine Learning Approach to Assist Navigation for People with Visual Impairments

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Abstract

An ever-increasing number of people are living with visual
impairments. As machine learning techniques evolve alongside improving
hardware available on embedded devices, there exists the potential to
develop a system which can detect and localise objects in an indoor setting.
This system would aim to detect objects and localise them, thereby
allowing the user to navigate around these obstacles in an unfamiliar environment.
The work presented here show the initial investigations into
the development of such a system. The information presented will cover
the investigation of a number of machine learning techniques as well as
the deployment of a model onto a constrained device.
Original languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems
Subtitle of host publicationContributions Presented at the 23rd UK Workshop on Computational Intelligence (UKCI 2024), September 2-4, 2024, Ulster University, Belfast, UK
EditorsHuiru Zheng, David Glass, Maurice Mulvenna, Jun Liu, Hui Wang
PublisherSpringer Cham
Pages157-169
Number of pages13
Volume1462
ISBN (Electronic)978-3-031-78857-4
ISBN (Print)978-3-031-78856-7
DOIs
Publication statusPublished online - 8 Jan 2025
Event23rd Annual UK Workshop on Computational Intelligence 2024 - Ulster University, Belfast, Belfast, Northern Ireland
Duration: 2 Sept 20244 Sept 2024
https://computing.ulster.ac.uk/ZhengLab/UKCI2024/

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer Chham
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Workshop

Workshop23rd Annual UK Workshop on Computational Intelligence 2024
Abbreviated titleUKCI 2024
Country/TerritoryNorthern Ireland
CityBelfast
Period2/09/244/09/24
Internet address

Keywords

  • Machine Learning
  • Deep Learning
  • Embedded Systems
  • The Edge
  • Neural Networks
  • Arduino

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