A CNN-Based Wearable Assistive System for Visually Impaired People Walking Outdoors

In this study, we propose an assistive system for helping visually impaired people walk outdoors. This assistive system contains an embedded system—Jetson AGX Xavier (manufacture by Nvidia in Santa Clara, CA, USA) and a binocular depth camera—ZED 2 (manufacture by Stereolabs in San Francisco, CA, US...

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Autores principales:	I-Hsuan Hsieh, Hsiao-Chu Cheng, Hao-Hsiang Ke, Hsiang-Chieh Chen, Wen-June Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	wearable device visually impaired people deep learning semantic segmentation depth map obstacle avoidance Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/18a3b01df8154a3f887a344fb1e5cfc9
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id	oai:doaj.org-article:18a3b01df8154a3f887a344fb1e5cfc9
record_format	dspace
spelling	oai:doaj.org-article:18a3b01df8154a3f887a344fb1e5cfc92021-11-11T15:06:35ZA CNN-Based Wearable Assistive System for Visually Impaired People Walking Outdoors10.3390/app1121100262076-3417https://doaj.org/article/18a3b01df8154a3f887a344fb1e5cfc92021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10026https://doaj.org/toc/2076-3417In this study, we propose an assistive system for helping visually impaired people walk outdoors. This assistive system contains an embedded system—Jetson AGX Xavier (manufacture by Nvidia in Santa Clara, CA, USA) and a binocular depth camera—ZED 2 (manufacture by Stereolabs in San Francisco, CA, USA). Based on the CNN neural network FAST-SCNN and the depth map obtained by the ZED 2, the image of the environment in front of the visually impaired user is split into seven equal divisions. A walkability confidence value for each division is computed, and a voice prompt is played to guide the user toward the most appropriate direction such that the visually impaired user can navigate a safe path on the sidewalk, avoid any obstacles, or walk on the crosswalk safely. Furthermore, the obstacle in front of the user is identified by the network YOLOv5s proposed by Jocher, G. et al. Finally, we provided the proposed assistive system to a visually impaired person and experimented around an MRT station in Taiwan. The visually impaired person indicated that the proposed system indeed helped him feel safer when walking outdoors. The experiment also verified that the system could effectively guide the visually impaired person walking safely on the sidewalk and crosswalks.I-Hsuan HsiehHsiao-Chu ChengHao-Hsiang KeHsiang-Chieh ChenWen-June WangMDPI AGarticlewearable devicevisually impaired peopledeep learningsemantic segmentationdepth mapobstacle avoidanceTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10026, p 10026 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	wearable device visually impaired people deep learning semantic segmentation depth map obstacle avoidance Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	wearable device visually impaired people deep learning semantic segmentation depth map obstacle avoidance Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 I-Hsuan Hsieh Hsiao-Chu Cheng Hao-Hsiang Ke Hsiang-Chieh Chen Wen-June Wang A CNN-Based Wearable Assistive System for Visually Impaired People Walking Outdoors
description	In this study, we propose an assistive system for helping visually impaired people walk outdoors. This assistive system contains an embedded system—Jetson AGX Xavier (manufacture by Nvidia in Santa Clara, CA, USA) and a binocular depth camera—ZED 2 (manufacture by Stereolabs in San Francisco, CA, USA). Based on the CNN neural network FAST-SCNN and the depth map obtained by the ZED 2, the image of the environment in front of the visually impaired user is split into seven equal divisions. A walkability confidence value for each division is computed, and a voice prompt is played to guide the user toward the most appropriate direction such that the visually impaired user can navigate a safe path on the sidewalk, avoid any obstacles, or walk on the crosswalk safely. Furthermore, the obstacle in front of the user is identified by the network YOLOv5s proposed by Jocher, G. et al. Finally, we provided the proposed assistive system to a visually impaired person and experimented around an MRT station in Taiwan. The visually impaired person indicated that the proposed system indeed helped him feel safer when walking outdoors. The experiment also verified that the system could effectively guide the visually impaired person walking safely on the sidewalk and crosswalks.
format	article
author	I-Hsuan Hsieh Hsiao-Chu Cheng Hao-Hsiang Ke Hsiang-Chieh Chen Wen-June Wang
author_facet	I-Hsuan Hsieh Hsiao-Chu Cheng Hao-Hsiang Ke Hsiang-Chieh Chen Wen-June Wang
author_sort	I-Hsuan Hsieh
title	A CNN-Based Wearable Assistive System for Visually Impaired People Walking Outdoors
title_short	A CNN-Based Wearable Assistive System for Visually Impaired People Walking Outdoors
title_full	A CNN-Based Wearable Assistive System for Visually Impaired People Walking Outdoors
title_fullStr	A CNN-Based Wearable Assistive System for Visually Impaired People Walking Outdoors
title_full_unstemmed	A CNN-Based Wearable Assistive System for Visually Impaired People Walking Outdoors
title_sort	cnn-based wearable assistive system for visually impaired people walking outdoors
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/18a3b01df8154a3f887a344fb1e5cfc9
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A CNN-Based Wearable Assistive System for Visually Impaired People Walking Outdoors

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