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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM43802

AI Powered Glasses for Visually Impaired People Using Object Detection

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM43802

Volume:

9 |

4 |

Apr 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM43802

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

SJ

Shivani Jangle1

DH

Divya Hutke2

TH

Tanmay Hiwanj3

AK

Ayush Kahar4

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Abstract

The advancement of artificial intelligence and machine learning has opened new possibilities for assistive technologies aimed at enhancing the lives of visually impaired individuals. This paper focuses on developing AI-powered smart glasses using Raspberry Pi and YOLOv8 object detection to provide real-time environmental awareness. The system incorporates a USB camera to capture surroundings, and a machine learning model for face recognition, which identifies known individuals and announces their names via an audio output. The audio signal is either played at a low volume or through earphones for private listening. The Raspberry Pi processes input data, detects objects and faces, and converts the visual information into speech, improving the user’s spatial understanding. This innovation fosters independent navigation and social interaction, offering a cost-effective solution for visually impaired individuals. By leveraging real-time object detection, the system enhances accessibility, enabling users to identify obstacles, recognize people, and interact more confidently with their surroundings.

KEYWORDS

Object Detection YOLOv8 AI Face Recognition

Cite This Article

Shivani Jangle1, Divya Hutke2, Tanmay Hiwanj3, Ayush Kahar4, "AI Powered Glasses for Visually Impaired People Using Object Detection," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 4, Apr 2025. https://doi.org/10.55041/IJSREM43802

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Publication Date

5/3/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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1

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2

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5

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The authors declare no conflict of interest related to this research work.

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