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Visual System01:26

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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Object detection and recognition: using deep learning to assist the visually impaired.

Abinash Bhandari1, P W C Prasad1, Abeer Alsadoon1

  • 1School of Computing and Mathematics, Charles Sturt University, Sydney, Australia.

Disability and Rehabilitation. Assistive Technology
|November 8, 2019
PubMed
Summary

Deep learning enhances navigation for the visually impaired by improving object detection. This review offers a framework and taxonomy for developing more robust and multifunctional assistive technologies.

Keywords:
Navigation assistanceRGB-D sensorclassifierdeep neural networkfeature extractionobstacle avoidancesemantic segmentationtraversability awarenessvisually-impaired people

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Assistive Technology

Background:

  • Deep learning significantly improves object detection accuracy across various applications, including medical aid and navigation for the visually impaired.
  • Existing systems effectively detect static obstacles but advancements are needed for dynamic environments.

Purpose of the Study:

  • To review deep learning systems utilized in navigational tools for the visually impaired.
  • To establish a framework guiding future research and development in this domain.

Main Methods:

  • Comparative analysis of current deep learning systems for visually impaired navigation.
  • Compilation of a taxonomy detailing essential features for advanced navigational systems.

Main Results:

  • The developed taxonomy of navigational systems demonstrates robustness for general application.
  • Identified challenges in object detection, particularly with moving or occluded objects.

Conclusions:

  • Deep learning offers cost-effective solutions for the visually impaired, with Convolutional Neural Networks (CNNs) and Fully Convolutional Neural Networks (FCNs) showing promise for multifunctional devices.
  • Future systems should prioritize enhanced user feedback mechanisms to overcome current technological limitations.