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.

翻译：本研究采用先进的YOLO模型（特别是YOLOv8、YOLOv7、YOLOv6和YOLOv5），探索了障碍物检测的综合方法。通过运用深度学习技术，研究重点比较了这些模型在实时检测场景中的性能表现。研究结果表明，YOLOv8在精确率-召回率指标上表现最优，实现了最高检测精度。本文详细阐述了训练流程、算法原理及系列实验结果，以验证模型的有效性。