Towards Robust Obstacle Avoidance for the Visually Impaired Person Using Stereo Cameras

We propose a novel obstacle avoidance strategy implemented in a wearable assistive device, which serves as an electronic travel aid (ETA), designed to enhance the safety of visually impaired persons (VIPs) during navigation to their desired destinations. This method is grounded in the assumption that objects in close proximity and within a short distance from VIPs pose potential obstacles and hazards. Furthermore, objects that are farther away appear smaller in the camera's field of view. To adapt this method for accurate obstacle selection, we employ an adaptable grid generated based on the apparent size of objects. These objects are detected using a custom lightweight YOLOv5 model. The grid helps select and prioritize the most immediate and dangerous obstacle within the user's proximity. We also incorporate an audio feedback mechanism with an innovative neural perception system to alert the user. Experimental results demonstrate that our proposed system can detect obstacles within a range of 20 m and effectively prioritize obstacles within 2 m of the user. The system achieves an accuracy rate of 95% for both obstacle detection and prioritization of critical obstacles. Moreover, the ETA device provides real-time alerts, with a response time of just 5 s, preventing collisions with nearby objects.