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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM56477
Volume:
10 |
2 |
Feb 2026
Pages:
Publication Frequency:
Monthly (12 issues per year)
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.
Visual impairment significantly limits an individual’s ability to perceive and interact with the surrounding environment, making independent navigation a daily challenge. To address this issue, this journal presents a vision-based assistive system designed to provide real-time environmental awareness and safety support for visually impaired users. The proposed system integrates a camera-based object detection module with distance sensing and audio feedback to convey meaningful information about nearby obstacles and objects. A pre-trained YOLO (You Only Look Once) deep learning model is employed for efficient and accurate object detection due to its high detection speed and capability to identify multiple objects in a single frame. To enhance safety during navigation, an ultrasonic sensor continuously measures the distance to nearby obstacles and triggers alerts when objects are detected within a predefined range. Detected information is communicated to the user through audio output, ensuring hands-free operation and minimizing cognitive load. In addition to environmental awareness, the system incorporates an emergency alert mechanism that allows the user to send their real-time location to a designated caregiver using a GPS and wireless communication module. This feature ensures immediate assistance during critical situations. The proposed system is designed to be portable, cost-effective, and user-friendly, making it suitable for real-world deployment. Experimental observations demonstrate that the system effectively improves situational awareness and enhances the independence and safety of visually impaired individuals.
KEYWORDS
Publication Date
11/1/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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