Vision Vanguard: A CNN-Based Solution for Visually Impaired

The Vision Vanguard project offers a cutting-edge CNN (Convolutional Neural Network) driven solution designed to strengthen the ranks of the visually disabled population. Using state-of-the-art image recognition techniques and instantaneous feedback, Vision Vanguard seeks to transform the everyday lives of those who are visually disabled, by giving them access to a dependable and user-friendly navigation and object recognition tool. The project intends to use artificial intelligence to help those with disabilities such as the blind or visually disabled perceive their environment. The CNN model is built with cutting-edge computer vision techniques and trained on a wide range of datasets with different environmental circumstances. Through the processing of input from a live video stream, the model produces written descriptions or aural feedback about its environment through an interface which maybe utilised by those with vision impairments. The study describes the CNN model's architecture and training process and goes over its performance measures, which include accuracy, precision, and recall. It also emphasizes the model's usefulness in various environmental settings and its capacity for real-time inference. The model is built using deep learning techniques and trained on a large and diverse dataset that encompasses both outdoor and indoor scenarios. Strict optimization and validation were used in the training process to achieve the ideal balance between accuracy and efficiency. The model's dependability and efficiency in identifying and categorizing objects, barriers, and contextual data are extensively tested and illustrated using performance measures including accuracy and precision. The research also explores the CNN model's real-time inference capabilities, showing how quickly it can digest visual input and provide textual explanations or aural feedback through an intuitive interface. Tests of robustness under several lighting and weather situations were carried out.