Independent pathfinding with collision avoidance for visually impaired individuals

Computer vision tasks such as image segmentation, object detection, and face recognition have been crucial in developing assistance systems for visually impaired individuals. Among these, image segmentation plays a vital role in helping them navigate safely. However, this task is more complex as it requires detailed spatial information. In this article, we propose a novel panoptic segmentation framework that serves as the foundation for an effective pathfinding system, combining robust collision avoidance with high performance. Our contribution includes a single-stage instance segmentation method built on a ResNet101-FPN encoder-decoder architecture. Additionally, we created a customized panoptic labeled dataset to meet the specific needs of visually impaired individuals, aiming to support future integration with real-time feedback in visual prostheses. We evaluate our model both qualitatively and quantitatively using the Panoptic Quality (PQ) metric. Results show that our method surpasses recent panoptic segmentation techniques, achieving a PQ score 4.092 points higher. It also outperforms existing pathfinding systems, demonstrating greater accuracy and efficiency under varied weather conditions.