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相关实验视频

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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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一个轻量级的检测模型,用于树枝上的gummosis,基于一个改进的YOLO算法.

Pingchuan Zhang1, Zeze Ma2, Ying Yang2

  • 1College of Computer Science and Technology, Henan Institute of Science and Technology, Henan, China. zhangpingc@hist.edu.cn.

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概括

一种新的轻量级YOLO-Gum模型可以准确地检测桃树菌,即使在难以看到的病变上也是如此. 这种先进的检测系统可以改善疾病管理,并支持植物健康的机器人视觉.

关键词:
在CCFM中,CCFM是最重要的.桃子的菌菌病.机器人视觉系统 机器人视觉系统这就是SENetV2的意义.智能农业智能农业树木的树枝树枝的树枝.

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科学领域:

  • 植物病理学 植物病理学
  • 计算机视觉 计算机视觉
  • 农业技术 农业技术

背景情况:

  • 菌是一种普遍的疾病,影响石头水果,特别是桃树,表现在树干和树枝上.
  • 由于复杂的形态和低差异化,直接观察高分支和干部病变具有挑战性.
  • 准确的检测对于有效预防和科学管理桃花菌至关重要.

研究的目的:

  • 开发一种轻量级且准确的桃树菌检测模型.
  • 为了解决观察和诊断桃树上gummosis病变的局限性.
  • 为自动化疾病检测和管理系统提供技术基础.

主要方法:

  • 将SENetV2模块集成到YOLOv8骨干中,以增强特征表示.
  • 将交叉尺度卷积特征融合模块 (CCFM) 引入部结构,以提高特征集成和效率.
  • 融合了SENetV2和CCFM,创建了轻量级的YOLO-Gum模型,优化了特征提取和检测精度.

主要成果:

  • 改进的YOLOv8n模型 (YOLO-Gum) 获得了92.5%的精度和74.3%的F1得分,超过了原来的YOLOv8n5.3%和6.2%.
  • 该模型显示了减少的参数 (2.79 M),更小的尺寸 (5.57 MB) 和更少的浮点运算 (7.6 G),代表了显著的效率增长.
  • 与基线YOLOv8n模型相比,检测准确度和计算效率有明显的改善.

结论:

  • 开发的YOLO-Gum模型是轻量级的,精确的,强大的,用于检测桃树菌.
  • 这个模型在检测性能和计算效率方面提供了显著的改进.
  • 为桃树健康管理和机器人疾病检测系统提供宝贵的技术支持.