Unimpeded Walking with Deep Learning

In this study, the detection of the objects that they may encounter with deep learning models and the methods of the tactile paving surface tracking with Hough’s theorem are presented so that visually impaired individuals can easily walk outdoors. In the proposed approach, the training is primarily realized for machine learning of the deep learning models. The Faster R-CNN model and the SSD MobileNetV2 model are used in the training, and the accuracy performances of these two models are compared. During the training phase of the two models, a data set is generated using real-time and internet-based photographs. The training is completed by making use of 3653 photographs for 11 different objects that visually impaired individuals may encounter. In the detection of the objects, the accuracy rate of Faster R-CNN model is approximately 91%, and the SSD MobileNetV2 model achieved approximately 93% success. In addition, with the help of Hough’s theorem, it is observed that the edge surface lines are followed correctly in the tracking of the tactile paving surfaces.