一个改进的YOLOv8n-IRP模型用于天然树的触摸表面检测和触摸关键点定位
Xirui Zhang1, Weiqiang Ma1, Junxiao Liu1
1School of Mechanical and Electrical Engineering, Hainan University, Haikou, China.
Frontiers in plant science
|November 14, 2024
概括
这项研究引入了一种改进的YOLOv8n-IRP模型,用于准确地检测和定位树林中的挖掘点. 改进后的模型在检测和定位方面表现出卓越的性能,支持自动拉操作.
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
- 农业技术 农业技术
背景情况:
- 精确检测和定位挖掘表面和关键点在复杂的森林环境中对轻量级算法来说具有挑战性.
- 现有的模型在低级别和高级别的特征中与特征提取和信息降解作斗争.
研究的目的:
- 提出一个改进的轻量级算法,YOLOv8n-IRP,用于更好地检测和定位接表面和接中的关键点.
- 为了改进功能提取,减少信息丢失,并增强关键点检测能力.
主要方法:
- 在脊柱网络中引入了一个受感场注意力机制,以改进特征提取.
- 使用了AFPN (自适应特征金字塔网络) 结构来最大限度地减少特征信息丢失.
- 设计了一个双分支关键点检测头,以更好地选关键点特征.
主要成果:
- 与原始模型相比,YOLOv8n-IRP提高了D_mAP50的1.4%和P_mAP50的2.3%.
- 在可变的照明条件下实现了87%的平均检测成功率,证明了强度.
- 在本地化方面表现优于YOLOv8n-Pose和YOLOv5n-Pose,平均欧几里德距离误差低于40像素.
结论:
- 该YOLOv8n-IRP模型表现出优异的检测和定位性能,用于接关键点.
- 这为接机器人中关键点定位提供了一种新的方法.
- 在智能系统中为无人皮操作提供技术支持.
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