University Archives

Mobility disabilities affect approximately one in eight adults, making them one of the most prevalent physical impairments globally. While technology continues to advance, many individuals still face significant barriers when navigating public spaces, particularly when operating doors. This project proposes an intelligent automated entry system designed to improve accessibility for disabled individuals, the elderly, and those with restricted hand mobility. By utilizing the You-Only-Look-Once (YOLO) deep learning architecture, the system performs real-time detection of specific mobility aids, such as wheelchairs, crutches, and walkers, alongside identifying elderly individuals or anyone with occupied hands. Unlike standard motion sensors, this computer vision approach provides a targeted response, ensuring doors open seamlessly for those requiring assistance. By integrating high-speed object detection into public infrastructure, this research aims to eliminate physical inconveniences and promote greater independence and inclusivity for vulnerable populations through autonomous, hands-free environmental interaction.