LoVi App: Android Application-based Image Classification for Low Vision
In Indonesia, many people with visual impairments are drawing public attention to their rights as fellow humans. One of the limitations that individuals with low vision face is their ability to recognize objects and navigate their surroundings due to difficulties in visual perception. In this moder...
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| Format: | Article |
| Language: | English |
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ITB Journal Publisher
2024-09-01
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| Series: | Journal of ICT Research and Applications |
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| Online Access: | https://journals.itb.ac.id/index.php/jictra/article/view/22132 |
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| author | Mitra Sofiyati Fandi Azam Wiranata Wervyan Shalannanda Eueung Mulyana Isa Anshori Ardianto Satriawan |
| author_facet | Mitra Sofiyati Fandi Azam Wiranata Wervyan Shalannanda Eueung Mulyana Isa Anshori Ardianto Satriawan |
| author_sort | Mitra Sofiyati |
| collection | DOAJ |
| description |
In Indonesia, many people with visual impairments are drawing public attention to their rights as fellow humans. One of the limitations that individuals with low vision face is their ability to recognize objects and navigate their surroundings due to difficulties in visual perception. In this modern era, deep learning technologies, especially in image classification, can help people with low vision overcome these challenges. In this paper, we discuss a deep learning system that optimizes image classification on users' smartphones to enhance visual support for individuals with low vision. We present an Android-based app, LoVi, designed to assist users with low vision. Powered by core systems within Sherpa models (TrotoarNet, IndoorNet, and CurrencyNet), LoVi has three modes: outdoor, indoor, and currency. The LoVi application provides over 80% accuracy for navigation on sidewalks, indoor object recognition, and currency identification. TrotoarNet aids in sidewalk navigation, IndoorNet assists with indoor object identification, and CurrencyNet recognizes Rupiah banknotes. Additionally, low-vision users can receive voice feedback for further accessibility.
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| format | Article |
| id | doaj-art-019bd46f090048a9a7d68dc62efccc19 |
| institution | DOAJ |
| issn | 2337-5787 2338-5499 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | ITB Journal Publisher |
| record_format | Article |
| series | Journal of ICT Research and Applications |
| spelling | doaj-art-019bd46f090048a9a7d68dc62efccc192025-08-20T03:05:46ZengITB Journal PublisherJournal of ICT Research and Applications2337-57872338-54992024-09-01182LoVi App: Android Application-based Image Classification for Low VisionMitra Sofiyati0Fandi Azam Wiranata1Wervyan Shalannanda2Eueung Mulyana3Isa Anshori4Ardianto Satriawan5School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Jalan Ganesa No. 10, Bandung 40132School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Jalan Ganesa No. 10, Bandung 40132School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Jalan Ganesa No. 10, Bandung 40132, Indonesia School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Jalan Ganesa No. 10, Bandung 40132School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Jalan Ganesa No. 10, Bandung 40132School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Jalan Ganesa No. 10, Bandung 40132 In Indonesia, many people with visual impairments are drawing public attention to their rights as fellow humans. One of the limitations that individuals with low vision face is their ability to recognize objects and navigate their surroundings due to difficulties in visual perception. In this modern era, deep learning technologies, especially in image classification, can help people with low vision overcome these challenges. In this paper, we discuss a deep learning system that optimizes image classification on users' smartphones to enhance visual support for individuals with low vision. We present an Android-based app, LoVi, designed to assist users with low vision. Powered by core systems within Sherpa models (TrotoarNet, IndoorNet, and CurrencyNet), LoVi has three modes: outdoor, indoor, and currency. The LoVi application provides over 80% accuracy for navigation on sidewalks, indoor object recognition, and currency identification. TrotoarNet aids in sidewalk navigation, IndoorNet assists with indoor object identification, and CurrencyNet recognizes Rupiah banknotes. Additionally, low-vision users can receive voice feedback for further accessibility. https://journals.itb.ac.id/index.php/jictra/article/view/22132convolutional neural networkdeep learningimage classificationlow visionsmartphone |
| spellingShingle | Mitra Sofiyati Fandi Azam Wiranata Wervyan Shalannanda Eueung Mulyana Isa Anshori Ardianto Satriawan LoVi App: Android Application-based Image Classification for Low Vision Journal of ICT Research and Applications convolutional neural network deep learning image classification low vision smartphone |
| title | LoVi App: Android Application-based Image Classification for Low Vision |
| title_full | LoVi App: Android Application-based Image Classification for Low Vision |
| title_fullStr | LoVi App: Android Application-based Image Classification for Low Vision |
| title_full_unstemmed | LoVi App: Android Application-based Image Classification for Low Vision |
| title_short | LoVi App: Android Application-based Image Classification for Low Vision |
| title_sort | lovi app android application based image classification for low vision |
| topic | convolutional neural network deep learning image classification low vision smartphone |
| url | https://journals.itb.ac.id/index.php/jictra/article/view/22132 |
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