Object Detection And Recognition Framework For The Visually-Impaired Using Deep Learning Techniques

Abstract

Visual impairment significantly affects an individual’s ability to perceive and interact with their surroundings, making everyday navigation and object recognition challenging. This project proposes an intelligent object detection and recognition framework using deep learning techniques to assist visually-impaired individuals in real time. The system utilizes advanced computer vision models such as Convolutional Neural Networks (CNN) and object detection algorithms like YOLO and SSD to identify objects from live camera feeds. Detected objects are converted into audio output using text-to-speech systems, enabling users to understand their environment. The framework aims to enhance independence, safety, and mobility by providing accurate and real-time object recognition. Experimental results indicate that deep learning-based approaches achieve high accuracy and efficiency, making them suitable for assistive technologies.