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International conference on the Sciences of Electronics, Technologies of Information and Telecommunications

SETIT 2018: Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.1 pp 364–371Cite as

Indoor Image Recognition and Classification via Deep Convolutional Neural Network

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 146))

Abstract

Indoor navigation (or way finding) still presents a great challenge for autonomous robotic systems and for visually impaired people (VIP). Indeed, the VIP is often enabling to see visual cues such as informational signs, landmarks or geometrical shapes. A Deep Convolution Neural Network (DCNN) has been proven to be highly effective and has achieved an outstanding success comparing to other techniques in object recognition. This paper proposes a robust approach for objects’ classification using a DCNN model. Experimental results in real indoor images with natural illumination (the MCIn-door 20000 dataset) show that the proposed DCNN model achieves the accuracy of 93.7% in objects classification.

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Author information

Authors and Affiliations

  1. Laboratory of Electronics and Microelectronics (EμE), Faculty of Sciences of Monastir, University of Monastir, 5000, 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. LITIS Laboratory & CNRS FR 3638, University of Rouen, Normandy, Rouen, France

    Edwige Pissaloux

Authors
  1. Mouna Afif
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  2. Riadh Ayachi
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  3. Yahia Said
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  4. Edwige Pissaloux
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  5. Mohamed Atri
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Corresponding author

Correspondence to Mouna Afif .

Editor information

Editors and Affiliations

  1. SETIT Lab, University of Sfax, Sfax, Tunisia

    Med Salim Bouhlel

  2. DIBRIS - University of Genoa, Genova, Genova, Italy

    Stefano Rovetta

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Afif, M., Ayachi, R., Said, Y., Pissaloux, E., Atri, M. (2020). Indoor Image Recognition and Classification via Deep Convolutional Neural Network. In: Bouhlel, M., Rovetta, S. (eds) Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.1. SETIT 2018. Smart Innovation, Systems and Technologies, vol 146. Springer, Cham. https://doi.org/10.1007/978-3-030-21005-2_35

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