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Updated: Jan 15, 2026

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在复杂的森林环境中使用轻量级YOLOv11-TrunkLight算法进行树干检测.

Siqi Zhang1, Yubi Zheng2, Rengui Bi3

  • 1College of Physics, Mechanical and Electrical Engineering, Jishou University, Jishou 416000, China.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了YOLOv11-TrunkLight,这是一种用于在森林中准确检测树干的轻量级算法. 它通过提高速度和减少计算负载来增强边缘设备上的机器人导航.

关键词:
这是一个有效的细节.星际网络 星际网络 星际网络YOLOv11 轻量级的模型双路特征脱的双路特征脱检查机器人检查机器人

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器人技术 机器人技术 机器人技术
  • 林业技术 林业技术 林业技术

背景情况:

  • 准确的树干检测对于在复杂的森林环境中自主导航机器人至关重要.
  • 现有的检测模型在资源有限的边缘设备上平衡准确性和实时性能方面面临挑战.

研究的目的:

  • 提出一种轻量级算法,YOLOv11-TrunkLight,用于在边缘设备上高效准确地检测干部.
  • 增强检查机器人的智能林业管理能力.

主要方法:

  • 开发了一个新的StarNet_Trunk骨干网络,利用元素智能乘法和深度可分离卷积.
  • 集成了C2DA可变形注意模块,以解决树干的几何变形.
  • 采用了EffiDet检测头,具有双路径特征解和动态 anchor机制,以提高效率.

主要成果:

  • 与基线YOLOv11.11相比,YOLOv11-TrunkLight在检测速度上实现了13.5%的增加.
  • 将参数数量减少了34.6%,计算负载 (FLOP) 减少了39.7%.
  • 在平均平均精度 (mAP) 中保持了仅为0.1%的边际下降.

结论:

  • 在资源有限的边缘设备上,YOLOv11-TrunkLight可以显著提高干部检测效率.
  • 该算法为增强自主导航和支持智能林业管理提供了可行的解决方案.