Assisting Blind People Using Object Detection with Vocal Feedback
Heba Najm Khirallah Elferjani Alhaam Alariyibi
For visually impaired people, it is highly difficult to make independent
movement and safely move in both indoors and outdoors environment. Furthermore,
these physically and visually challenges prevent them from in day-today live
activities. Similarly, they have problem perceiving objects of surrounding
environment that may pose a risk to them. The proposed approach suggests
detection of objects in real-time video by using a web camera, for the object
identification, process. You Look Only Once (YOLO) model is utilized which is
CNN-based real-time object detection technique. Additionally, The OpenCV
libraries of Python is used to implement the software program as well as deep
learning process is performed. Image recognition results are transferred to the
visually impaired users in audible form by means of Google text-to-speech
library and determine object location relative to its position in the screen.
The obtaining result was evaluated by using the mean Average Precision (mAP),
and it was found that the proposed approach achieves excellent results when it
compared to previous approaches.