Artificial Intelligence for Smart Cities and Villages: Advanced Technologies, Development, and Challenges

Indoor Object Classification System using Neural Networks for Smart Environments

Author(s): Mouna Afif*, Riadh Ayachi and Mohamed Atri

Pp: 105-115 (11)

DOI: 10.2174/9789815049251122010009

* (Excluding Mailing and Handling)


Building new systems used for indoor assistance navigation and wayfinding
in indoor places present a crucial and primary step to contributing to smart indoor
environments. Indoor objects recognition and classification using deep neural networks
(DNNs) present very powerful tools to assist blind and sighted persons during their
indoor navigation. This chapter proposes to develop a new indoor assistance navigation
system using deep convolutional neural networks. The proposed system was evaluated
using different types of deep learning-based models. The developed system can be
highly recommended to contribute to a smart environment and to be applied for smart
homes applications. Experiments conducted in this work have shown the efficiency and
the robustness of the developed indoor object classification system. Experiment results
obtained are very competitive in terms of classification rates which come up to 99.9%.

Keywords: Assistance navigation, Deep Convolutional Neural Networks, Indoor object recognition, Smart environments.

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