  The utilization of deep learning-based object detection is an effective
approach to assist visually impaired individuals in avoiding obstacles. In this
paper, we implemented seven different YOLO object detection models
\textit{viz}., YOLO-NAS (small, medium, large), YOLOv8, YOLOv7, YOLOv6, and
YOLOv5 and performed comprehensive evaluation with carefully tuned
hyperparameters, to analyze how these models performed on images containing
common daily-life objects presented on roads and sidewalks. After a systematic
investigation, YOLOv8 was found to be the best model, which reached a precision
of $80\%$ and a recall of $68.2\%$ on a well-known Obstacle Dataset which
includes images from VOC dataset, COCO dataset, and TT100K dataset along with
images collected by the researchers in the field. Despite being the latest
model and demonstrating better performance in many other applications, YOLO-NAS
was found to be suboptimal for the obstacle detection task.
