Navigation Using Object Detection for Visually Impaired Individuals

This study investigates new methods of applying object detection logic to improve how visually impaired persons navigate through their environment. The progress of computer vision and machine learning provides object detection with an essential function to monitor environments instantly so visually impaired users can safely move through their surroundings. The research integrates YOLO (You Only Look Once), Faster R-CNN and similar deep learning object detection models with wearable technology to transfer spatial data through imaginative acoustic and sensory feedback methods. The proposed system detects vital objects in changing environments and delivers prompt and correct notifications about these objects. This manuscript details the design approaches and implementation strategy alongside performance analysis of the navigation system throughout controlled conditions and real environments to evaluate stability alongside precision outcomes and user experience. The results indicate that object detection technologies could create substantial improvements in both mobility independence and daily activities for visually impaired patients.