Implicit and Explicit Knowledge_Based Deep learning Technique for Indoor Wayfinding Assistance Navigation

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Abstract
Indoor objects and recognition present a very important task in artificial intelligence (AI) and computer vision fields. This task is an increasingly important especially for blind and visually impaired (BVI) indoor assistance navigation. An increasing interest is addressed for building new assistance technologies used to improve the daily life technologies used to improve the daily life activities qualities for BVI persons. To fulfill this need we propose in this work a new deep learning based techniques used for indoor wayfinding assistance navigation. we propose to use in this paper a new deep learning-based technique based on You Only Learn One Representation YOLOR network. This network enables a combination between implicit and explicit learning and knowledge just like the human brain can do. By introducing the implicit knowledge, the neural network is able to generate a unified representation that can serve for different tasks. In order to train and test the proposed indoor wayfinding assistance system, we proposed to work with the proposed indoor signage dataset. Based on the conducted experiments, the proposed indoor wayfinding system has demonstrated very interesting results. We applied different optimizations techniques in order to reduce the network size and parameters number to make the proposed model suitable for implementation on embedded devices. As a detection performance, we obtained 95.62% mAP for the original version of YOLOR network and 93.12% mAP for the compressed version and 28 FPS as detection speed.

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