| dc.description.abstract | According to the World Health Organization＇s statistics in 2019, there are approximately 2.2 billion people worldwide with visual impairment issues, and there are about 56,000 visually impaired people in Taiwan. For visually impaired people, it is quite difficult to move independently in unfamiliar environments, and traditional aids such as white canes and guide dogs have their own difficulties or difficulties in popularization. Therefore, this paper proposes to use a stereoscopic camera and deep learning algorithm to assist visually impaired people in avoiding obstacles, detecting road signs, and assisting them in walking in unfamiliar environments.
This paper includes: (1) developing an offline indoor navigation aid, (2) using MobileNet to detect road conditions, and (3) using three models, YOLO, CRAFT, and CRNN, to analyze road sign information and assist the visually impaired in moving around public indoor spaces.
Although DenseNet (94.58%) performed better than MobileNet (93.53%) in road detection experiments, MobileNet with fewer parameters is more suitable considering hardware devices. In the experiment of using YOLO to detect road signs, when IOU>0.5, the mAP is 90.7%, which can already detect road signs to assist the visually impaired in moving around. | en_US |