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

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 mAP@0.95 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.