对比四款YOLO模型，YOLOv8在障碍物检测中表现最佳。
Innovative Deep Learning Techniques for Obstacle Recognition: A Comparative Study of Modern Detection Algorithms
- 采用YOLOv5/v6/v7/v8四模型，对比其障碍物检测性能。
- YOLOv8精度最高，召回率与精确率均优于其他模型。
- 适合自动驾驶、机器人避障等实时检测场景使用。
本研究探讨了基于先进YOLO模型（YOLOv8、YOLOv7、YOLOv6、YOLOv5）的障碍物检测方法，聚焦于这些模型在实时检测场景下的性能比较。通过深入的训练过程、算法原理分析及大量实验数据，验证了所提方法的有效性。结果表明，YOLOv8在准确率方面表现最优，其精确率与召回率指标均优于其他模型，在复杂环境下的检测稳定性也显著提升。
原文摘要 · Abstract (English)
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.
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