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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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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