Investigating YOLO Models Towards Outdoor Obstacle Detection For Visually Impaired People
This work addresses obstacle detection for visually impaired individuals, but it is incremental as it compares existing models without introducing new methods.
The paper evaluated seven YOLO object detection models for outdoor obstacle detection to assist visually impaired people, finding YOLOv8 as the best with 80% precision and 68.2% recall on a combined dataset.
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.