An Real Time Object Detection Method for Visually Impaired Using Machine Learning

Vision, one of the five fundamental human senses, is crucial for defining how people perceive the objects around them. Visual impairments affect more than 200 million people worldwide, severely limiting their ability to perform numerous activities of daily living. Thus, it is essential for blind people to understand their surroundings and the objects they are interacting with. In this work, we created a tool that helps blind persons recognize diverse items in their environment by utilizing the YOLO V3 algorithm combined with R-CNN. This comprises a variety of approaches to develop an app that not only instantly recognizes different objects in the visually impaired person’s environment but also guides them using audio output. A convolutional neural network (CNN) called YOLO (You Only Look at Once) recognizes objects in real time. This suggested method is more effective and accurate than other algorithms for recognizing things, according to research results, and it produces results for object detection that are extremely similar in real time. It is crucial for persons who are blind or visually impaired to be able to reliably and effectively detect and recognize objects in order to navigate both common and unfamiliar situations safely, become stronger, and become more independent.