Innovative Deep Learning Techniques for Obstacle Recognition: A Comparative Study of Modern Detection Algorithms
It addresses obstacle detection for real-time applications, but is incremental as it compares existing models.
This study compared YOLOv8, YOLOv7, YOLOv6, and YOLOv5 for real-time obstacle detection, finding that YOLOv8 achieved the highest accuracy with improved precision-recall metrics.
This study explores a comprehensive approach to obstacle detection using advanced YOLO models, specifically YOLOv8, YOLOv7, YOLOv6, and YOLOv5. Leveraging deep learning techniques, the research focuses on the performance comparison of these models in real-time detection scenarios. The findings demonstrate that YOLOv8 achieves the highest accuracy with improved precision-recall metrics. Detailed training processes, algorithmic principles, and a range of experimental results are presented to validate the model's effectiveness.