Indoor Object Classification System using Neural Networks for Smart Environments
Mouna Afif, Riadh Ayachi, Mohamed Atri · BENTHAM SCIENCE PUBLISHERS eBooks · 2022
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%.