A Deep Learning Approach to Sensory Navigation Device for Blind Guidance
Josh Jia-Ching Ying, Chenyu Li, Guan-Wei Wu, Jianxing Li, Wei-Jheng Chen, Don‐Lin Yang · 2018
Sensory navigation device is an important trend in the field of machine learning and data science. Nowadays, more and more sensory navigation devices are built for blind people. The core of such sensory navigation devices for blind people usually is implemented by an Image Recognition Method. To build an image recognition model, many tools and online machine learning platforms are proposed. However, these tools or platforms are not able to completely satisfy the requirements for sensory navigation device. To build a sensory navigation device with satisfying requirements for blind people, an ability of reducing the cost of model training and a capability of user-centric image recognition are the two main issues. Therefore, to address the above issues, we propose a novel approach, namely, DLSNF (Deep-Learning-based Sensory Navigation Framework). Our proposed DLSNF is built based on the YOLO architecture to deal with the reducing cost of model training and NVIDIA Jetson TX2 to take the user-centric image recognition into account. Based on our proposed DLSNF, the real-time image recognition can be trained well and conduct a sensory navigation to help blind people. At the same time, the train model is embedded in NVIDIA Jetson TX2 which is the fastest, most power-efficient embedded AI computing device. For the experiments, we evaluated our proposed DLSNF with a real-world dataset consisting of 4,570 images collected by part-time workers. The extensive experimental results show that our proposed DLSNF more effectively and efficiently beyond the existing baselines.