Vision Transformer Customized for Environment Detection and Collision Prediction to Assist the Visually Impaired Article Swipe
Nasrin Bayat
,
Jong-Hwan Kim
,
Renoa Choudhury
,
Ibrahim F. Kadhim
,
Zubaidah Al-Mashhadani
,
Mark Aldritz Dela Virgen
,
Reuben Latorre
,
Ricardo De La Paz
,
Joon‐Hyuk Park
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/jimaging9080161
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/jimaging9080161
This paper presents a system that utilizes vision transformers and multimodal feedback modules to facilitate navigation and collision avoidance for the visually impaired. By implementing vision transformers, the system achieves accurate object detection, enabling the real-time identification of objects in front of the user. Semantic segmentation and the algorithms developed in this work provide a means to generate a trajectory vector of all identified objects from the vision transformer and to detect objects that are likely to intersect with the user’s walking path. Audio and vibrotactile feedback modules are integrated to convey collision warning through multimodal feedback. The dataset used to create the model was captured from both indoor and outdoor settings under different weather conditions at different times across multiple days, resulting in 27,867 photos consisting of 24 different classes. Classification results showed good performance (95% accuracy), supporting the efficacy and reliability of the proposed model. The design and control methods of the multimodal feedback modules for collision warning are also presented, while the experimental validation concerning their usability and efficiency stands as an upcoming endeavor. The demonstrated performance of the vision transformer and the presented algorithms in conjunction with the multimodal feedback modules show promising prospects of its feasibility and applicability for the navigation assistance of individuals with vision impairment.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jimaging9080161
- https://www.mdpi.com/2313-433X/9/8/161/pdf?version=1692107120
- OA Status
- gold
- Cited By
- 9
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385834161
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385834161Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/jimaging9080161Digital Object Identifier
- Title
-
Vision Transformer Customized for Environment Detection and Collision Prediction to Assist the Visually ImpairedWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-15Full publication date if available
- Authors
-
Nasrin Bayat, Jong-Hwan Kim, Renoa Choudhury, Ibrahim F. Kadhim, Zubaidah Al-Mashhadani, Mark Aldritz Dela Virgen, Reuben Latorre, Ricardo De La Paz, Joon‐Hyuk ParkList of authors in order
- Landing page
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https://doi.org/10.3390/jimaging9080161Publisher landing page
- PDF URL
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https://www.mdpi.com/2313-433X/9/8/161/pdf?version=1692107120Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2313-433X/9/8/161/pdf?version=1692107120Direct OA link when available
- Concepts
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Computer science, Usability, Audio feedback, Computer vision, Collision, Transformer, Artificial intelligence, Object detection, Segmentation, Real-time computing, Human–computer interaction, Engineering, Computer security, Voltage, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 3, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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