Indoor Object Classification System using Neural Networks for Smart Environments
- Authors: Mouna Afif1, Riadh Ayachi2, Mohamed Atri3
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View Affiliations Hide Affiliations1 Laboratory of Electronics and Microelectronics (EE), Faculty of Sciences of Monastir,University of Monastir, Tunisia 2 Laboratory of Electronics and Microelectronics (EE), Faculty of Sciences of Monastir,University of Monastir, Tunisia 3 College of Computer Science, King Khalid University, Abha, Saudi Arabia
- Source: Artificial Intelligence for Smart Cities and Smart Villages: Advanced Technologies, Development, and Challenges , pp 105-115
- Publication Date: August 2022
- Language: English
Indoor Object Classification System using Neural Networks for Smart Environments, Page 1 of 1
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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%.
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