Use of a neural network in creating a digital assistant for blind and visually impaired people

Abstract

The experience of ongoing research in image processing clearly demonstrates the huge scope for the development of new neural networks that can help people in a wide range of tasks. The authors chose the direction of work related to helping people who have vision problems. The article considers a convolutional neural network of the Mask R-CNN model for segmenting objects in an image. During the research the authors study a large number of algorithms that can quickly and accurately process images, such as Faster R-CNN, which was the most efficient in 2020. During the analysis, it was revealed that the use of Mask R-N technology can significantly increase the efficiency of performing tasks, since this algorithm is the latest version of the machine learning model. As a result of the study, a neural network was developed that is capable of identifying and distinguishing a large number of objects in an image. The next step is to refine the algorithm and use additional means of interaction with the hardware of the systems to increase the speed of the neural network. In the future, the resulting neural network will be integrated into the Digital Assistant for the Blind and Visually Impaired Persons application. This application is guaranteed to improve the daily life of people with disabilities who experience certain inconveniences due to their features, and can become the basis for other, larger projects related, for example, to unmanned devices, as well as services whose work is directly based on image processing.

Keywords

• digital assistant
• machine learning
• neural networks
• mask r-cnn architecture
• convolutional network
• search algorithm
• segmentation
• classification
• selective search
• image processing
• vision problems
• visual impairment
• assistance to the disabled
• electronic assistant