Smart Glass System Using Deep Learning for the Blind and Visually Impaired

Individuals suffering from visual impairments and blindness encounter difficulties in moving independently and overcoming various problems in their routine lives. As a solution, artificial intelligence and computer vision approaches facilitate blind and visually impaired (BVI) people in fulfilling t...

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Autores principales:	Mukhriddin Mukhiddinov, Jinsoo Cho
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	smart glasses artificial intelligence blind and visually impaired deep learning low-light images assistive technologies Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/d40be9b6b49d41eb8836a0adb2d199e0
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id	oai:doaj.org-article:d40be9b6b49d41eb8836a0adb2d199e0
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spelling	oai:doaj.org-article:d40be9b6b49d41eb8836a0adb2d199e02021-11-25T17:24:23ZSmart Glass System Using Deep Learning for the Blind and Visually Impaired10.3390/electronics102227562079-9292https://doaj.org/article/d40be9b6b49d41eb8836a0adb2d199e02021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/22/2756https://doaj.org/toc/2079-9292Individuals suffering from visual impairments and blindness encounter difficulties in moving independently and overcoming various problems in their routine lives. As a solution, artificial intelligence and computer vision approaches facilitate blind and visually impaired (BVI) people in fulfilling their primary activities without much dependency on other people. Smart glasses are a potential assistive technology for BVI people to aid in individual travel and provide social comfort and safety. However, practically, the BVI are unable move alone, particularly in dark scenes and at night. In this study we propose a smart glass system for BVI people, employing computer vision techniques and deep learning models, audio feedback, and tactile graphics to facilitate independent movement in a night-time environment. The system is divided into four models: a low-light image enhancement model, an object recognition and audio feedback model, a salient object detection model, and a text-to-speech and tactile graphics generation model. Thus, this system was developed to assist in the following manner: (1) enhancing the contrast of images under low-light conditions employing a two-branch exposure-fusion network; (2) guiding users with audio feedback using a transformer encoder–decoder object detection model that can recognize 133 categories of sound, such as people, animals, cars, etc., and (3) accessing visual information using salient object extraction, text recognition, and refreshable tactile display. We evaluated the performance of the system and achieved competitive performance on the challenging Low-Light and ExDark datasets.Mukhriddin MukhiddinovJinsoo ChoMDPI AGarticlesmart glassesartificial intelligenceblind and visually impaireddeep learninglow-light imagesassistive technologiesElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2756, p 2756 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	smart glasses artificial intelligence blind and visually impaired deep learning low-light images assistive technologies Electronics TK7800-8360
spellingShingle	smart glasses artificial intelligence blind and visually impaired deep learning low-light images assistive technologies Electronics TK7800-8360 Mukhriddin Mukhiddinov Jinsoo Cho Smart Glass System Using Deep Learning for the Blind and Visually Impaired
description	Individuals suffering from visual impairments and blindness encounter difficulties in moving independently and overcoming various problems in their routine lives. As a solution, artificial intelligence and computer vision approaches facilitate blind and visually impaired (BVI) people in fulfilling their primary activities without much dependency on other people. Smart glasses are a potential assistive technology for BVI people to aid in individual travel and provide social comfort and safety. However, practically, the BVI are unable move alone, particularly in dark scenes and at night. In this study we propose a smart glass system for BVI people, employing computer vision techniques and deep learning models, audio feedback, and tactile graphics to facilitate independent movement in a night-time environment. The system is divided into four models: a low-light image enhancement model, an object recognition and audio feedback model, a salient object detection model, and a text-to-speech and tactile graphics generation model. Thus, this system was developed to assist in the following manner: (1) enhancing the contrast of images under low-light conditions employing a two-branch exposure-fusion network; (2) guiding users with audio feedback using a transformer encoder–decoder object detection model that can recognize 133 categories of sound, such as people, animals, cars, etc., and (3) accessing visual information using salient object extraction, text recognition, and refreshable tactile display. We evaluated the performance of the system and achieved competitive performance on the challenging Low-Light and ExDark datasets.
format	article
author	Mukhriddin Mukhiddinov Jinsoo Cho
author_facet	Mukhriddin Mukhiddinov Jinsoo Cho
author_sort	Mukhriddin Mukhiddinov
title	Smart Glass System Using Deep Learning for the Blind and Visually Impaired
title_short	Smart Glass System Using Deep Learning for the Blind and Visually Impaired
title_full	Smart Glass System Using Deep Learning for the Blind and Visually Impaired
title_fullStr	Smart Glass System Using Deep Learning for the Blind and Visually Impaired
title_full_unstemmed	Smart Glass System Using Deep Learning for the Blind and Visually Impaired
title_sort	smart glass system using deep learning for the blind and visually impaired
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d40be9b6b49d41eb8836a0adb2d199e0
work_keys_str_mv	AT mukhriddinmukhiddinov smartglasssystemusingdeeplearningfortheblindandvisuallyimpaired AT jinsoocho smartglasssystemusingdeeplearningfortheblindandvisuallyimpaired
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Smart Glass System Using Deep Learning for the Blind and Visually Impaired

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