Enhancing Outdoor Mobility and Environment Perception for Visually Impaired Individuals Through a Customized CNN-based System

Abstract

Visual impairment indicates any kind of vision loss including blindness. Individuals with visual impairments face significant challenges when trying to perceive their surroundings from a global perspective and navigating unfamiliar environments. Existing assistive technologies predominantly focus on obstacle avoidance, neglecting to provide comprehensive information about the overall environment. To address this gap, the proposed system employs a customized Convolutional Neural Network (CNN) model tailored to accurately predict the type of outdoor ground terrain the user is traversing. This information is then conveyed to the user audibly. It can also detect the presence of puddles on the road and let the user know whether the outside floor is wet (slippery). The proposed deep-learning architecture is trained on images collected from sources including the Stagnant Water dataset, the GTOS-Mobile dataset and a custom dataset. The trained model is then integrated into an Android app, providing visually impaired (VI) people with effective surrounding perception capabilities, leading to better travel and, ultimately, better living.