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<p>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&#xa0;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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月19日

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

摘要

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.