Enhancing Mobility and Independence of Visually Impaired Individuals through Mobile-Based Real-Time Obstacle Detection Systems

Abstract

Independent mobility is a critical determinant of social health and Quality of Life for individuals with visual impairments, yet physical barriers and limitations in traditional aids often lead to restricted travel, contributing significantly to loneliness, social exclusion, and heightened risks of depression and anxiety. This paper systematically analyzes the development and implementation challenges of Mobile-Based Real-Time Obstacle Detection Systems as a pivotal technological intervention designed to overcome these barriers. Successful RT-ODS relies on highly optimized technical architectures, such as the lightweight YOLOv8 deep learning model, tailored for efficient real-time inference on resource-constrained mobile platforms. Empirical evidence demonstrates the feasibility of achieving robust performance, with some systems attaining an accuracy greater than 90% and a mAP less than 0.5, under varying environmental conditions. Crucially, the adoption and long-term efficacy of these systems are contingent upon addressing socio-economic and ethical constraints. User-centric design requires integrating multimodal feedback (auditory and haptic), while economic accessibility demands low production costs to serve a population often facing financial vulnerability. This synthesis concludes that Real-Time Obstacle Detection Systems, when developed with a comprehensive interdisciplinary approach that balances technical optimization, cost-effectiveness, and rigorous ethical compliance, offers a viable, scalable pathway to significantly enhance the confidence, independence, and social integration of visual impairments individuals.