Improving Freedom of Visually Impaired Individuals with Innovative EfficientNet and Unified Spatial-Channel Attention

Individuals with visual impairments often encounter substantial challenges navigating outdoor spaces due to their inability to perceive road-surface conditions. This study introduces an innovative method that harnesses deep learning to identify and categorize road surfaces, aiming to enhance the independence and mobility of the visually impaired. Leveraging the EfficientNetB0 model as a foundational framework and employing unified spatial-channel attention, we classified road surface images captured from a wearable camera. Through rigorous training and evaluation on a substantial dataset of road images, our modified system exhibited remarkable performance, accurately identifying road surfaces with an impressive 99.39% accuracy rate. This deep learning-driven approach holds promise as a pivotal tool for improving the autonomy and safety of individuals with visual challenges by providing instantaneous feedback on road conditions.