Enhancing Urban Mobility: Deep Learning-Based Traffic Sign Assistant for the Visually Impaired

Urban mobility presents significant challenges for visually impaired individuals, particularly in interpreting and navigating through traffic signs. In response, this paper introduces a novel Traffic Sign Assistant (TSA) system empowered by deep learning technology to aid the visually impaired in comprehending traffic signs and enhancing their mobility in urban environments. The proposed TSA system leverages state-of-the-art deep learning models, notably convolutional neural networks (CNNs), for real-time detection and recognition of traffic signs. Through robust deep learning algorithms, the system can accurately identify various types of traffic signs, including regulatory, warning, and informational signs, overcoming challenges such as variations in lighting conditions and occlusions. Evaluation of the proposed TSA system involves comprehensive testing and user feedback sessions to assess its effectiveness in real-world scenarios. Results demonstrate the system’s ability to significantly improve the mobility and safety of visually impaired individuals in urban environments by providing timely and accurate interpretation of traffic signs. This research contributes to the advancement of assistive technologies tailored to the needs of the visually impaired, offering a promising solution to enhance urban mobility and accessibility through the integration of deep learning-based traffic sign assistance systems.