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 c...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-01529-7 |
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| _version_ | 1849387871072419840 |
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| author | Kevin Muñoz Mario Chavarria Luisa Ortiz Silvan Suter Klaus Schönenberger Bladimir Bacca-Cortes |
| author_facet | Kevin Muñoz Mario Chavarria Luisa Ortiz Silvan Suter Klaus Schönenberger Bladimir Bacca-Cortes |
| author_sort | Kevin Muñoz |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-be4bab81d22244d7a0938c8e7e0835fa |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-be4bab81d22244d7a0938c8e7e0835fa2025-08-20T03:42:28ZengNature PortfolioScientific Reports2045-23222025-05-0115111910.1038/s41598-025-01529-7Embedded solution to detect and classify head level objects using stereo vision for visually impaired people with audio feedbackKevin Muñoz0Mario Chavarria1Luisa Ortiz2Silvan Suter3Klaus Schönenberger4Bladimir Bacca-Cortes5School of Electrical and Electronic Engineering, Universidad del ValleSwiss Federal Institute of Technology Lausanne, EssentialTechFaculty of Engineering and Basic Sciences, Universidad Autónoma de OccidentSwiss Federal Institute of Technology Lausanne, EssentialTechSwiss Federal Institute of Technology Lausanne, EssentialTechSchool of Electrical and Electronic Engineering, Universidad del ValleAbstract 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.https://doi.org/10.1038/s41598-025-01529-7Audio feedbackConvolutional neural networksEmbedded systemsHead-level object detectionVisually impaired people |
| spellingShingle | Kevin Muñoz Mario Chavarria Luisa Ortiz Silvan Suter Klaus Schönenberger Bladimir Bacca-Cortes Embedded solution to detect and classify head level objects using stereo vision for visually impaired people with audio feedback Scientific Reports Audio feedback Convolutional neural networks Embedded systems Head-level object detection Visually impaired people |
| title | Embedded solution to detect and classify head level objects using stereo vision for visually impaired people with audio feedback |
| title_full | Embedded solution to detect and classify head level objects using stereo vision for visually impaired people with audio feedback |
| title_fullStr | Embedded solution to detect and classify head level objects using stereo vision for visually impaired people with audio feedback |
| title_full_unstemmed | Embedded solution to detect and classify head level objects using stereo vision for visually impaired people with audio feedback |
| title_short | Embedded solution to detect and classify head level objects using stereo vision for visually impaired people with audio feedback |
| title_sort | embedded solution to detect and classify head level objects using stereo vision for visually impaired people with audio feedback |
| topic | Audio feedback Convolutional neural networks Embedded systems Head-level object detection Visually impaired people |
| url | https://doi.org/10.1038/s41598-025-01529-7 |
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