YOLO glass: video-based smart object detection using squeeze and attention YOLO network

Visually impairments or blindness people need guidance in order to avoid collision risks with outdoor obstacles. Recently, technology has been proving its presence in all aspects of human life, and new devices provide assistance to humans on a daily basis. However, due to real-time dynamics or a lack of specialized knowledge, object detection confronts a reliability difficulty. To overcome the challenge, YOLO Glass a Video-based Smart object detection model has been proposed for visually impaired person to navigate effectively in indoor and outdoor environments. Initially the captured video is converted into key frames and pre-processed using Correlation Fusion-based disparity approach. The pre-processed images were augmented to prevent overfitting of the trained model. The proposed method uses an obstacle detection system based on a Squeeze and Attendant Block YOLO Network model (SAB-YOLO). A proposed system assists visually impaired users in detecting multiple objects and their locations relative to their line of sight, and alerts them by providing audio messages via headphones. The system assists blind and visually impaired people in managing their daily tasks and navigating their surroundings. The experimental results show that the proposed system improves accuracy by 98.99%, proving that it can accurately identify objects. The detection accuracy of the proposed method is 5.15%, 7.15% and 9.7% better that existing YOLO v6, YOLO v5 and YOLO v3, respectively.