基于无人机的高速公路应急检查的YOLO系列算法的比较分析:性能和应用洞察力
Ziyi Yang1, Xin Lan1, Hui Wang2
1College of Artificial Intelligence, Southwest University, Chongqing 400715, China.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
这项研究比较了无人机高速公路危险检测 (HDD) 的YOLO算法. YOLOv11-n在效率和模型尺寸方面表现出色,因此非常适合实时应用.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 运输工程 运输工程
背景情况:
- 基于无人驾驶飞行器 (UAV) 的高速公路应急检测 (HDD) 在准确性和效率方面提出了挑战.
- YOLO (你只看一次) 算法系列为自动化硬盘提供了潜在的解决方案.
研究的目的:
- 对基于无人机的硬盘进行各种YOLO算法的比较分析.
- 为选择高速公路基础设施监控的最佳YOLO模型提供参考.
主要方法:
- 对YOLOv5,YOLOv7,YOLOv8,YOLOv9和YOLOv10算法进行比较评估.
- 检测精度 (精度,回忆) 和检测效率 (速度,模型大小,计算复杂性) 的评估.
主要成果:
- YOLOv5-l和YOLOv9-c显示了最高的检测准确度.
- 在检测效率方面,YOLOv10-n,YOLOv7-t和YOLOv11-n领先.
- 对于实时硬盘,YOLOv11-n提供了效率,小型模型尺寸和低计算复杂性的最佳平衡.
- YOLOv5-n和YOLOv8-n显示了最高的相对压缩度.
结论:
- YOLOv11-n是一个非常有前途的模型,用于嵌入式实时无人机基于高速公路危险检测.
- 模型架构,特别是YOLOv9中的骨干变化和YOLOv5中的深度/宽度倍数,显著影响了准确性.
- 特定的YOLO版本在HDD应用中为准确性,效率或模型大小提供了明显的优势.
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