Real-Time Object Detection and Face Recognition Application for the Visually Impaired

The advancement of navigation systems for the visually impaired has significantly enhanced their mobility by mitigating the risk of encountering obstacles and guiding them along safe, navigable routes. Traditional approaches primarily focus on broad applications such as wayfinding, obstacle detection, and fall prevention. However, there is a notable discrepancy in applying these technologies to more specific scenarios, like identifying distinct food crop types or recognizing faces. This study proposes a real-time application designed for visually impaired individuals, aiming to bridge this research-application gap. It introduces a system capable of detecting 20 different food crop types and recognizing faces with impressive accuracies of 83.27% and 95.64%, respectively. These results represent a significant contribution to the field of assistive technologies, providing visually impaired users with detailed and relevant information about their surroundings, thereby enhancing their mobility and ensuring their safety. Additionally, it addresses the vital aspects of social engagements, acknowledging the challenges faced by visually impaired individuals in recognizing acquaintances without auditory or tactile signals, and highlights recent developments in prototype systems aimed at assisting with face recognition tasks. This comprehensive approach not only promises enhanced navigational aids but also aims to enrich the social well-being and safety of visually impaired communities.