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The utilization of deep learning-based object detection is an effectiveapproach to assist visually impaired individuals in avoiding obstacles. In thispaper, we implemented seven different YOLO object detection models\textit{viz}., YOLO-NAS (small, medium, large), YOLOv8, YOLOv7, YOLOv6, andYOLOv5 and performed comprehensive evaluation with carefully tunedhyperparameters, to analyze how these models performed on images containingcommon daily-life objects presented on roads and sidewalks. After a systematicinvestigation, YOLOv8 was found to be the best model, which reached a precisionof $80\%$ and a recall of $68.2\%$ on a well-known Obstacle Dataset whichincludes images from VOC dataset, COCO dataset, and TT100K dataset along withimages collected by the researchers in the field. Despite being the latestmodel and demonstrating better performance in many other applications, YOLO-NASwas found to be suboptimal for the obstacle detection task.

Investigating YOLO Models Towards Outdoor Obstacle Detection For Visually Impaired People