Work

Embedded solution to detect and classify head level objects using stereo vision for visually impaired people with audio feedback

Year: 2025

Type: article

Abstract: This work presents an embedded solution for detecting and classifying head-level objects using stereo vision to assist blind individuals. A custom dataset was created, featuring five classes of head-level objects, selected based on a survey of visually impaired users. Object detection and classification were achieved using deep-neural networks such as YoloV5. The system computes the relative range and orientation of detected head-level objects and provides audio feedback to alert the user about nearby objects. Four types of tests were conducted: a dataset-based test, achieving a [email protected] of 0.89 for head-level objects classification; a quantitative assessment of range and orientation, with an average error of 0.028 m ± 0.004 and 2.05°±0.09, respectively; a field test conducted over a week at different times and lighting conditions, yielding a precision/recall of 98.21%/93.75% for head-level object classification; and user tests with Head-level identification accuracy of 91% and obstacle-avoidance/local-navigation where users reported an average of 88.75% for low or middle risk.

Source: Scientific Reports

DOI: 10.1038/s41598-025-01529-7

Authors: Kevin Muñoz, Mario Chavarria, Luisa Ortiz, Silvan Suter, Klaus Schönenberger, Bladimir Bacca-Cortes

Institutions: Universidad del Valle, École Polytechnique Fédérale de Lausanne, Universidad Autónoma de Occidente

Language: English

Topic: Assistive Technology in Communication and Mobility

Subfield: Occupational Therapy

Field: Health Professions

Domain: Health Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: gold

Estimated APC paid: $2,690