Vision: A Deep Learning Approach to provide walking assistance to the visually impaired
This work addresses mobility challenges for visually impaired people, but it is incremental as it applies existing deep learning techniques to a specific domain.
The paper tackles the problem of assisting visually impaired individuals in navigating their surroundings by developing a system that uses YOLO for object detection and monocular vision for depth estimation to provide audio guidance for obstacle avoidance, achieving higher accuracy compared to stereo vision methods.
Blind people face a lot of problems in their daily routines. They have to struggle a lot just to do their day-to-day chores. In this paper, we have proposed a system with the objective to help the visually impaired by providing audio aid guiding them to avoid obstacles, which will assist them to move in their surroundings. Object Detection using YOLO will help them detect the nearby objects and Depth Estimation using monocular vision will tell the approximate distance of the detected objects from the user. Despite a higher accuracy, stereo vision has many hardware constraints, which makes monocular vision the preferred choice for this application.