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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
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Peer Review: Double Blind
Volume 02, Issue 6

Published on: June 2026

SMART SYSTEM FOR VISUALLY IMPAIRED PEOPLE

Pradeep Patil Pranali Karale Shruti Pawar Srushti Salunkhe Sanika Sawale

Department of Information Technology Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering and Technology

Baramati-413133, Dist-Pune, Maharashtra, India
DOI:https://doi.org/10.55041/ijcope.v2i6.279

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Abstract

Visual impairment presents profound chal-lenges to independent navigation, particularly in dynamic urban environments. Smart system For Visually Impaired People is a real time AI-powered mobile application built with Flutter that helps visually impaired indi viduals perceive their surroundings through an ensemble of on-device intelligence mod ules. The system integrates a YOLOv8 model compiled to TensorFlow Lite format for detecting eighty COCO object classes, a Sobel edge-analysis pipeline for recognis ing doors and staircases, Google ML Kit for on-device optical character recognition, a continuous speech recognition listener for voice command input, and a priority-driven text-to-speech engine for natural-language audio output. Every inference pipeline exe cutes locally on the Android device, eliminating dependence on network connectivity and preserving user privacy. A non-maximum suppression stage duplicates overlapping de tections, and a per label cooldown mechanism prevents repetitive announcements while still allowing the user to invoke an instant-repeat command. Hazardous objects detected on the user’s direct path trigger both an emergency voice alert and a haptic vibration, providing redundant warnings. Distance estimation is performed through a per-class bounding-box area calibration model, giving users approximate proximity information in metres.This app demonstrates that affordable hardware with on-device AI can effec tively assist visually impaired users.

Index Terms—Deep Learning, YOLOv8, TensorFlow Lite, Flut-ter, Object Detection, Visually Impaired, Assistive Technology, COCO Dataset, Text-to-Speech, Optical Character Recognition, Sobel Edge Detection, On-Device AI

How to Cite this Paper

Patil, P., Karale, P., Pawar, S., Salunkhe, S. & Sawale, S. (2026). Smart System for Visually Impaired People. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(6). https://doi.org/10.55041/ijcope.v2i6.279

Patil, Pradeep, et al.. "Smart System for Visually Impaired People." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i6.279.

Patil, Pradeep,Pranali Karale,Shruti Pawar,Srushti Salunkhe, and Sanika Sawale. "Smart System for Visually Impaired People." International Journal of Creative and Open Research in Engineering and Management 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i6.279.

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References


  1. Najm, K. Elferjani, and A. Alariyibi, “Assisting Blind People Using Object Detection with Vocal Feedback,” in Proc. IEEE 2nd Int. Maghreb Meeting Conf. Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), Sabratha, Libya, 2022, pp. 1–5, doi: 10.1109/MI-STA54861.2022.9837737.

  2. Ashiq, M. Asif, M. B. Ahmad, S. Zafar, K. Masood, T. Mahmood,



  1. T. Mahmood, and I. H. Lee, “CNN-Based Object Recognition and Tracking System to Assist Visually Impaired People,” IEEE Access, vol. 10, pp. 14819–14834, 2022, doi: 10.1109/ACCESS.2022.3148036.



  1. K. Pongiannan, J. Franklin, A. R. Pravin, M. Maria, and S. K. Mishra, “Object Detection Using Deep Learning for Blind People with Voice Feedback,” in Proc. Int. Conf. System, Computation, Au-tomation and Networking (ICSCAN), 2024, pp. 1–6, doi: 10.1109/IC-SCAN62807.2024.10894354.

  2. Alagarsamy, D. Usharani, K. P. L. Syamala, and K. Balaji, “A Real Time Object Detection Method for Visually Impaired Using Machine Learning,” in Proc. Int. Conf. Computer Communication and Informatics (ICCCI), 2023, pp. 1–6, doi: 10.1109/ICCCI56745.2023.10128388.

  3. S. Saini, “Deep Residual Network for Image Recognition,” in Proc. IEEE Int. Conf. Distributed Computing and Electrical Cir-cuits and Electronics (ICDCECE), 2022, pp. 1–4, doi: 10.1109/ICD-CECE53908.2022.9792645.

  4. Mart´ınez-Cruz, L. A. Morales-Herna´ndez, G. I. Pe´rez-Soto, J. P. Benitez-Rangel, and K. A. Camarillo-Go´mez, “An Outdoor Navigation Assistance System for Visually Impaired People in Public Transporta-tion,” IEEE Access, vol. 9, pp. 130767–130777, 2021, doi: 10.1109/AC-CESS.2021.3111544.

  5. D. Akkara and A. Kuriakose, “Smartphone Apps for Visually Impaired Persons,” Kerala Journal of Ophthalmology, vol. 31, no. 3, pp. 242–248, 2019.

  6. Sultana, A. Sufian, and P. Dutta, “A Review of Object Detection Mod-els Based on Convolutional Neural Network,” in Intelligent Computing: Image Processing Based Applications, Springer, 2020, pp. 1–16.

  7. -J. Chang, L.-B. Chen, C.-Y. Sie, and C.-H. Yang, “An Artificial Intelligence Edge Computing-Based Assistive System for Visually Im-paired Pedestrian Safety at Zebra Crossings,” IEEE Trans. Consumer Electronics, vol. 67, no. 1, pp. 3–11, 2021.

  8. C. Wong, J. A. Lai, S. S. S. Ranjit, A. R. Syafeeza, and N. A. Hamid, “Convolutional Neural Network for Object Detection System for Blind People,” Journal of Telecommunication, Electronic and Computer Engineering (JTEC), vol. 11, no. 2, 2019.

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  • •Published on: Jun 22 2026
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