YOLOv11: A Next-Generation Approach to Real-Time Object Detection

Vision impairment is one of the top ten disabilities that can be affected by humans, with India having the highest visually impaired population. This study introduces a novel framework utilizing YOLOv11 for real-time object detection and recognition for the objects and to assist the visually impaired individuals in navigating their surroundings independently. The YOLOv11 detector, trained on the COCO dataset with an additional class for enhanced detection, enables precise classification of various objects. The system integrates Python and OpenCV to detect objects from videos and webcams, ultimately providing audio feedback to the user. The proposed system ensures fast and accurate detection, improving accessibility and reducing dependency on external assistance.