Obstacle Detection and Distance Estimation for Visually Impaired People

Abstract

In the realm of assistive technologies for visually impaired persons (VIPs), existing solutions such as white canes and guide dogs have limitations in range and practicality. Moreover, current electronic systems often fall short in terms of portability and the ability to estimate distances in real-Time. To bridge these gaps, this study introduces a revolutionary wearable device comprising a Raspberry Pi, a camera module, and a pretrained convolutional neural network, all integrated into a pair of smart glasses. These glasses are designed to identify objects and estimate their distances from the wearer, providing real-Time auditory or haptic feedback. The development process was rigorous, involving the deployment of machine learning algorithms for object identification and the integration of camera and sensor technology into a lightweight, user-friendly frame. The system's performance was extensively evaluated using quantitative metrics, showing its precision, speed, and usability. Conclusively, this study presents a significant leap in wearable assistive technologies, offering enhanced spatial awareness, autonomy, and quality of life for VIPs.