Volume 22, Issue 4 (December 2026)                   IJEEE 2026, 22(4): 4202-4202 | Back to browse issues page

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Deyasi A, Barai D, Debnath P. Real-Time Detection of Common Objects and Humans with Relative Distance Measurement using a pre-trained AI Model for Visually Impaired Persons. IJEEE 2026; 22 (4) :4202-4202
URL: http://ijeee.iust.ac.ir/article-1-4202-en.html
Real-Time Detection of Common Objects and Humans with Relative Distance Measurement using a pre-trained AI Model for Visually Impaired Persons
Arpan Deyasi , Diptadip Barai , Pampa Debnath
Abstract:   (562 Views)
The present paper introduces a real-time intelligent assistive system that uses a pre-trained Artificial Intelligence (AI) model to estimate relative distance and detect common items/living beings and known people to help visually impaired people. A lightweight and accurate object identification framework based on the YOLOv8 algorithm is used in the proposed system and tailored for embedded and portable devices to facilitate real-time detection. The system continually analyses live video data, recognizes objects and people within its range of vision, even when moving towards the impaired person with a significant relative velocity, and uses depth mapping or stereo vision algorithms to determine their relative distances. Relative distance is measured with accuracy varying from 98.66% to 99.8% for static objects and 98.67% to 99.47% for dynamic objects and both indoor and outdoor environments, with maximum variance of 1.21 and 5.76, respectively, for static and moving objects, far superior compared to earlier results for distances greater than 200 cm. With an approximate 30% improvement in confidence score for static objects, with 2.5% enhancement in mean average precision and 15% improvement in FPS, the proposed study presents a practical and scalable assistive solution that integrates AI perception and distance awareness to support independent navigation for visually impaired users.
Keywords: Pre-trained AI Model, Relative Distance Measurement, Real-Time Detection, Visually Impaired People.
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Type of Study: Research Paper | Subject: Integrated Circuits: Digital, Analog
Received: 2025/11/04 | Revised: 2026/09/18 | Accepted: 2026/06/29
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.