Assistive Object Recognition System for Visually Impaired

The issue of visual impairment or blindness is faced worldwide. According to statistics of the World Health Organization (WHO), globally, at least 2.2 billion people have a vision impairment or blindness, of whom at least 1 billion are blind. In terms of regional differences, the prevalence of vision impairment in lowand middle-income regions is four times higher than in high-income regions.[6] Blind people generally have to rely on white canes, guide dogs, screen-reading software, magnifiers, and glasses to assist them for mobility, however, To help the blind people the visual world has to be transformed into the audio world with the potential to inform them about objects as well as their spatial locations. Therefore, we propose to aid the visually impaired by introducing a system that is most feasible, compact, and costeffective. So, we implied a system that makes use of Raspberry Pi in which you only look once (YOLO v3) machine learning algorithm trained on the coco database is applied. The experimental result shows YOLO v3 achieves state-of-the-art results of 85% to 95% on overall performance, 100% (person, chair, clock, and cell-phone) recognition accuracy. This system not only provides mobility to the visually impaired with that it provides the term that ahead is an XYZ object rather than a