Transfer Learning based Computer Vision Technology for Assisting Visually Impaired for detection of Common Places

Visually impaired people face several problems in their daily life. One of the biggest problems is to visiting unfamiliar places and identifying public places like pharmacy store, restrooms, pedestrian signs on roads, etc. Although there are some conventional methods that are available to aid visually impaired people but these are inefficient to use without assistance. The proposed method presents a framework which will help visually impaired people to identify the common public amenities while visiting any unfamiliar places. This method uses deep learning for recognizing some daily used places. For this purpose, VGG16 model is used to extract features from the images and train the sequential model. The model has been tested on varying images of different class that are present in the database. The developed algorithm achieves an accuracy of 95.88%. The obtained result of the developed model shows that it is an efficient method for assisting visually impaired people in real time application.