Advancing Accessibility: An Artificial Intelligence Framework for Obstacle Detection and Navigation Assistance for the Visually Impaired

Abstract

The white cane has long been a fundamental tool for individuals with visual impairments, aiding in surface detection and obstacle identification. However, its limitations in detecting moving objects and distant obstacles pose significant safety risks, particularly in congested areas and busy streets. While service animals offer an alternative, they come with training challenges and high costs. To address these limitations and enhance safety, this paper proposes a comprehensive collision detection and prevention system. The proposed system integrates cutting-edge technologies, including image processing, deep learning, Internet of Things (IoT), cloud computing, and audio production devices. By combining these technologies with the white cane, the system offers a sophisticated navigation option for the visually impaired, effectively detecting and preventing potential collisions. In busy environtment scenarios, the system proves its effectiveness by complementing the white cane's use, overcoming its inherent limitations, and significantly improving navigation capabilities. Through this innovative approach, blind individuals gain enhanced situational awareness, empowering them to navigate diverse environments with increased confidence and safety. By mitigating the drawbacks of the white cane, the proposed system provides a comprehensive and cost-effective solution to enhance the mobility and safety of the visually impaired. This research contributes to the advancement of assistive technologies, offering a valuable resource for researchers, policymakers, and practitioners in the field of accessibility and inclusive design.