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Deep learning-based application for indoor wayfinding assistance navigation

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Abstract

There is an increasing need to develop new adaptive technologies and new wayfinding assistance systems for blind and visually impaired persons in order to improve their daily lives. To address this need, we propose in this paper to develop a new deep learning-based indoor wayfinding assistance system consisting of detecting landmark indoor signs. Assistive technologies used for blind and sighted persons used to support daily activities to improve social inclusion are developing very fast. Training and testing experiments were performed on the proposed indoor signage dataset. Through the experiments conducted, we demonstrated the efficiency of the proposed indoor wayfinding aid system. We obtained 93.45% as a mean average precision (mAP) of the proposed indoor wayfinding and signage detection system.

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Authors and Affiliations

  1. Laboratory of Electronics and Microelectronics (EμE), Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia

    Mouna Afif, Riadh Ayachi, Yahia Said & Mohamed Atri

  2. Electrical Engineering Department, College of Engineering, Northern Border University, Arar, Saudi Arabia

    Yahia Said

  3. College of Computer Science, King Khalid University, Abha, Saudi Arabia

    Mohamed Atri

Authors
  1. Mouna Afif
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  2. Riadh Ayachi
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  3. Yahia Said
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  4. Mohamed Atri
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Correspondence to Mouna Afif.

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Afif, M., Ayachi, R., Said, Y. et al. Deep learning-based application for indoor wayfinding assistance navigation. Multimed Tools Appl 80, 27115–27130 (2021). https://doi.org/10.1007/s11042-021-10999-6

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  • DOI: https://doi.org/10.1007/s11042-021-10999-6

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