Enhancing Object Recognition for Visually Impaired Individuals using Computer Vision
Enhancing Object Recognition for Visually Impaired Individuals using Computer Vision |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-4 |
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| Year of Publication : 2024 | ||
| Author : Myo Min Aung, Dechrit Maneetham, Padma Nyoman Crisnapati, Yamin Thwe |
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| DOI : 10.14445/22315381/IJETT-V72I4P130 | ||
How to Cite?
Myo Min Aung, Dechrit Maneetham, Padma Nyoman Crisnapati, Yamin Thwe, "Enhancing Object Recognition for Visually Impaired Individuals using Computer Vision," International Journal of Engineering Trends and Technology, vol. 72, no. 4, pp. 297-305, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I4P130
Abstract
In the realm of computer vision and autonomous systems, object recognition and obstacle recognition are pivotal tasks, each contributing uniquely to the intelligent capabilities and safety of people and mobile robots. While object recognition focuses on identifying and classifying objects within digital images or video frames, obstacle recognition is dedicated to detecting and localizing obstacles or hazards in an environment. Object recognition, which utilizes machine learning, computer vision, YOLOv4 architecture, and the COCO dataset, is presented with a particular emphasis on visually impaired individuals. This study integrates YOLOv4 and the COCO dataset, aiming to advance object recognition while harnessing the benefits of obstacle recognition. The research encompasses hardware implementation, including a Raspberry Pi with an added 7-inch LCD and software implementation involving machine learning models. Test results reveal the system's robustness and real-time functionality. Furthermore, the user experience testing at the exhibition of Phramongkutklao Hospital garnered positive feedback, which is valuable input to build a user-centric approach in developing object recognition technology tailored to their needs. This research promises valuable contributions to intelligent systems' object recognition in complex environments.
Keywords
Computer Vision, Object recognition, Raspberry-pi, Vision impairment, YOLOv4.
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