IRO Journals
Journal of Soft Computing Paradigm

AI-Based Real-Time Blind Navigation System

Open Access
Published: 11 May, 2026
Pages: 131-142
  • Ananthakumari A. , Ananthakumari A.
    Assistant Professor, Department of Computer Science and Engineering, Dr. G U Pope College of Engineering, Thoothukudi, India
    Assistant Professor, Department of Computer Science and Engineering, Dr. G U Pope College of Engineering, Thoothukudi, India
  • Kezia A. , Kezia A.
    Student, Department of Computer Science and Engineering, Dr. G U Pope College of Engineering, Thoothukudi, India
    Student, Department of Computer Science and Engineering, Dr. G U Pope College of Engineering, Thoothukudi, India
  • Jothi Lakshmi E. , Jothi Lakshmi E.
    Student, Department of Computer Science and Engineering, Dr. G U Pope College of Engineering, Thoothukudi, India
    Student, Department of Computer Science and Engineering, Dr. G U Pope College of Engineering, Thoothukudi, India
  • Saranya L. Saranya L.
    Student, Department of Computer Science and Engineering, Dr. G U Pope College of Engineering, Thoothukudi, India
    Student, Department of Computer Science and Engineering, Dr. G U Pope College of Engineering, Thoothukudi, India
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How to Cite

A., Ananthakumari, Kezia A., Jothi Lakshmi E., and Saranya L. 2026. “AI-Based Real-Time Blind Navigation System”. Journal of Soft Computing Paradigm 8 (2): 131-42. https://doi.org/10.36548/jscp.2026.2.003.
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Keywords

Blind Navigation
Real-Time Object Detection
YOLOv8
Voice Guidance
Emergency Alert
Assistive Technology

Abstract

This research work integrates advanced computer vision algorithms, real-time object detection by the YOLOv8 algorithm, and distance measurement techniques for real-time blind navigation system. With the help of these techniques, the proposed framework continuously processes images for recognizing objects including pedestrians, cars, walls, doors, and any other obstacles that can harm the user. Using the information regarding the detected obstacles along with their distances, the system produces voice guidance to help the user make the right decisions about movement and safety measures. This project uses Python, OpenCV, and Streamlit, providing a cheaper, lighter, and easier-to-use alternative to hardware-based navigation devices. This method will provide efficient and timely aid in detecting obstacles and navigating through them, which will enhance the freedom, safety, and independence of visually impaired people. Furthermore, this system has potential applications in developing smarter assistive technologies, which could be extended in the future to include GPS, smartphone apps, and route planning features.

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