A Study on Deep Learning based Blind Guidance Model Building for Smart Device
| Autor: | LI, CHEN-YU, 李振宇 |
|---|---|
| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 In the field of machine learning and data science, smart navigation device is a trend. More and more smart devices are designed for blind people, and the core of the visual aid is usually realized by image recognition. There are many tools and machine learning platforms for the construction of image recognition model, but these tools couldn’t satisfy the requirements of the visual aid devices. In order to construct a visual aid device, an ability of reducing the cost of model training and portability of the image recognition visual aid device are the main problems. System needs to remind and warn user in immediate danger situation, such as the car, the traffic light, and the indoor space obstacle or helping blind people find indoor objects efficiently and quickly. Therefore, in order to solve these problems, we proposed deep learning-based Blind Guidance framework. YOLO (Real-Time Object Detection) is currently the fastest detection, high accuracy and low training detection method, which is used to reduce the cost of model training. For portability of the image recognition visual aid device, the system needs to calculate deep learning and low power capability embedded devices, NVIDIA Jetson TX2 solves the problem of portability image recognition equipment. For the experiments, we evaluated our proposed framework with 4,853 images. The experimental results show that our proposed system suitable for blind people currently. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
| Externí odkaz: |
|