(untitled) (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Amongst the world’s total population there exist 285 million visually impaired people. There exist several types of equipment which can lead them to a safe way but are restricted to indoor areas and are high priced. In this paper, a wearable device called VISUAL DUX is developed that helps the blind by detecting the objects in their path and directing them through the freeway either indoor or outdoor in a cost-efficient way. As object detection works better with the deep learning algorithms we have used the YOLO V3 dataset as input to the model. The YOLO method uses convolutional neural networks to detect objects by preparing bounding boxes. After getting the bounding boxes, classes of the objects are identified based on the probabilities of the bounding boxes. After finding the class of the object the direction of the obstacle is detected to direct the people with no vision with a safer path. Compared to the existing devices our device gives better accuracy and not only detecting the obstacle it also gives the safer direction to move as an audio output.