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基于改进的YOLOv5s的水培中国开花白菜检测和本地化算法.

Zhongjian Xie1, Yaya Zhang1, Weilin Wu1,2

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概括

本研究介绍了一种两阶段算法,用于检测和定位水培中国开花白菜,增强自动收获. 改进的P-YOLOv5s-GRNF和YOLOv5s-SBC模型实现了更高的准确性和效率.

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科学领域:

  • 农业工程 农业工程
  • 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

背景情况:

  • 水耕作物的自动收获需要精确的检测和植物的定位.
  • 现有的物体检测算法可能缺乏实时农业应用所需的效率和准确性.
  • 水栽培中国开花白菜对自动收获系统提出了独特的挑战.

研究的目的:

  • 开发一个强大的两阶段算法,用于检测和定位水培中国开花白菜.
  • 为了提高对象检测模型的性能,用于增强的自动收获系统.
  • 为水栽培中智能化和自动化收获解决方案提供技术基础.

主要方法:

  • 提出了一个宏观检测算法,P-YOLOv5s-GRNF,结合修剪,GSConv,受感场注意力卷积 (RFAConv),基于正常化的注意力模块 (NAM) 和焦点-EIOU损失.
  • 开发了一个微本地化算法,YOLOv5s-SBC,具有调整的检测层配置,加权双向特征金字塔网络 (BiFPN) 和协调注意力 (CA) 机制.
  • 将改进的模型与深度摄像机集成,以创建定位系统.

主要成果:

  • 与各种主流物体检测算法相比,P-YOLOv5s-GRNF显示了显著的mAP (平均精度) 改进.
  • 与原来的YOLOv5s.GRNF相比,P-YOLOv5s-GRNF实现了降低参数 (18%),模型大小 (11.9MB) 和FLOP (14.5G),同时FPS增加了4.3%,与原来的YOLOv5s.GRNF相比.
  • 与原始YOLOv5s相比,YOLOv5s-SBC增加了4.0%的mAP,并大幅减少了参数 (65%),模型大小 (60%) 和FLOP (15.3G).

结论:

  • 拟议的P-YOLOv5s-GRNF和YOLOv5s-SBC算法显著提高了水培中国开花白菜的检测和定位准确性和效率.
  • 这些优化模型有助于在受控农业环境中开发实用和智能自动化收获系统.
  • 开发的定位系统为推进水栽培中自动化和智能化收获提供了宝贵的技术支持.