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Deep Learning Based Application for Indoor Scene Recognition

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Abstract

Recognizing indoor scene and objects and estimating their poses present a wide range of applications in robotic field. This task becomes more challenging especially in cluttered environments like the indoor scenery. Scaling up convnets presents a key component in achieving better accuracy results of deep convolutional neural networks. In this paper, we make use of the rethinked efficient neural networks and we fine-tune them in order to develop a new application used for indoor object and scene recognition system. This new application will be especially dedicated for blind and visually impaired persons to explore new indoor environments and to fully integrate in daily life. The proposed indoor object and scene recognition system achieves new state-of-the-art results in MIT 67 indoor dataset and in scene 15 dataset. We obtained 95.60% and 97% respectively as a recognition rate.

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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

  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 Scene Recognition. Neural Process Lett 51, 2827–2837 (2020). https://doi.org/10.1007/s11063-020-10231-w

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