Framework for Object Recognition and Detection for Blind Users Using Deep Learning

Abstract

People who are visually impaired struggle with many aspects of daily life. For people who are blind, being able to detect and recognize familiar objects in the environment seems like a terrific idea. The main obstacle to object recognition in this difficult development requires computer vision that interacts with internal and external classes. This phenomenon calls for additional attention over time. In this study, we present a novel framework to support object detection and recognition for the blind and visually impaired, enabling them to move independently and be aware of their environment. Utilizing a distinctive discriminating mechanism, things are recognized utilizing previous implementation techniques. The acquisition of machine learning and vision technologies, creative technology, forward-thinking, and the appropriate response based on its accuracy and efficacy can be used to characterize a multi-label technique in this situation. In the proposed study, we use type/grouping strategies to address issues with current tools. These techniques can speed up the detection of many items while maintaining best-in-class time complexity. Additionally, the model that aids blind people can understand independently the items and money around, and they can hear about them through auditory feedback. The output of the framework can also be provided to the blind individual in audio format.