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VISIONAI:REAL-TIME OBJECT DETECTION WITH AUDIO ASSISTANCE FOR VISUALLY IMPAIRED PEOPLE

AUTHORS:
Akilan A
Mentor
Dr. S.V.Anandhi
Affiliation
Department of Artificial Intelligence and Data Science Ramco Institute of Technology, Rajapalayam
DOI: 10.55041/ijsmt.v2i3.131
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

According to the World Health Organization, 2.2 billion people are visually impaired worldwide, and 1 billion of the visually impaired cannot move around their environments using conventional mobility aids. In this study, the author suggests a real-time object detector audio assistant called VisionAI, which will assist visually impaired persons in moving around in a self-sufficient way by continually surveying the environment. The proposed solution is based on real-time video streaming at 1280720 pixels and identifying 80 classes of objects using the YOLOv10 model in 4.2 ms/frame. Objects are made to measure the distance using the Pinhole Camera Model, which estimates the distance at an average error of 0.5 m. The horizontal frame consists of three areas, and it identifies the direction of objects with an accuracy rate of 93 percent. The solution proposed here applies a three-level urgency classification model called CRITICAL, HIGH, and MODERTE to categorize objects based on their distance. Audio-visual feedback was delivered based on pyttsx3, text-to-speech, Web Audio API, and React 18 front-end. The solution is based on FastAPI, WebSocket, and React stack, and the latency is less than one second between end-to-end, and the detection of the object is 91 percent in clear and 84 percent in the low-light environment.

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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 03
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Mar 18 2026
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A, A. (2026). Visionai:Real-Time Object Detection with Audio Assistance for Visually Impaired People. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.131

A, Akilan. "Visionai:Real-Time Object Detection with Audio Assistance for Visually Impaired People." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.131.

A, Akilan. "Visionai:Real-Time Object Detection with Audio Assistance for Visually Impaired People." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.131.

References
1.Yadav, N., Kumawat, S., Mishra, S. K., Jaseja, M., & Khare, S. (2025, February). A Real-Time Object Detection System with Audio Feedback for Visually Impaired Persons. In 2025 International Conference on Intelligent Control, Computing and Communications (IC3) (pp. 16-19). IEEE.DOI: 10.1109/IC363308.2025.10957036

2.Ben Rhouma, R., & da Silva, F. O. (2025, June). Assistive Mobile Application for Visually Impaired Individuals Using Real-Time Object Recognition with Voice Feedback. In Iberian Conference on Information Systems and Technologies (pp. 883-894). Cham: Springer Nature Switzerland.

3. Dhaarini, G., Sanjai, R., & Sandosh, S. (2025, May). Real-Time Sign Language Detection and Assistive System Using YOLOv10 for Enhanced Communication and Learning. In 2025 7th International Conference on Energy, Power and Environment (ICEPE) (pp. 1-6). IEEE. DOI: 10.1109/ICEPE65965.2025.11139585

4.Nedjar, I., Bekkaoui, M., Hacene, L. F. B., M’hamedi, M., Bedaif, F., & Benzineb, M. A. (2026). Assistive Device for Visually Impaired Individuals Featuring Road Object Detection. Arabian Journal for Science and Engineering, 1-20.

5.Ali, A. (2025). Performance Analysis of Deep Learning Object Detection Models for Visually Impaired (Master's thesis, Itä-Suomen yliopisto). DOI: 10.1109/ICSCDS65426.2025.11167768

6. Gugulothu, S. S., Shou, A., Awte, A., Ninawe, S., & Bhalerao, M. (2025, August). Smart Low-Light Outdoor Object Detection System based on YOLOv10 for Visual Aid. In 2025 3rd International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) (pp. 1804-1809). IEEE. DOI: 10.1109/ICSCDS65426.2025.11167768

7. Bougheloum, L., Bousbia Salah, M., & Bettayeb, M. (2025). Real-time object detection for visually impaired people using an improved yolov7-plus architecture. Arabian Journal for Science and Engineering, 1-18.

8.Gobika, B., Megha, S., Sivasakthi, V., & Tharageswari, K. (2025, February). A New Hybrid Assistive Module Towards Text Recognition and Speech Conversion System from Real Time Acquired Images to Visually Impaired Peoples. In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL) (pp. 1375-1379). IEEE. DOI: 10.1109/ICSADL65848.2025.10933072

9.Arora, M., Gupta, S., Raj, M., Kumar, A., & Tripathi, D. (2025, July). AI-Powered Object Detection and Feedback System for the Visually Impaired. In International Conference on Data Science and Applications (pp. 422-433). Cham: Springer Nature Switzerland.

10. Pujari, V., Madnal, K., & Premchandran, D. (2024, November). Mobile app for enhancing accessibility among the visually impaired. In 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI) (pp. 1-6). IEEE. 10.1109/IDICAIEI61867.2024.10842749
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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