使用修改后的YOLOv8-Seg模型进行叶片分割
Peng Wang1,2,3, Hong Deng1,3, Jiaxu Guo4
1College of Arts and Sciences, Northeast Agricultural University, Harbin 150030, China.
Life (Basel, Switzerland)
|June 27, 2024
概括
这项研究使用计算机视觉增强了植物叶片细分. 修改后的YOLOv8模型与Ghost和BiFPN模块实现了86.4%的子得分,提高了精准农业的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 植物表型化 植物表型化
- 农业技术 农业技术
背景情况:
- 自动化植物叶片细分对于植物分类,生长监测和精准农业至关重要.
- 现有的计算机视觉模型需要改进,以提高细分精度,特别是对于小或重叠的叶子.
研究的目的:
- 使用计算机视觉技术改进自动化植物叶片细分.
- 评估将幽灵和双向特征金字塔网络 (BiFPN) 模块集成到YOLOv8-seg模型中的有效性.
主要方法:
- 用YOLOv8-seg模型作为叶片细分的基线.
- 提出了两个修改后的YOLOv8-seg架构,包括用于高效的特征生成的Ghost模块和用于多尺度特征融合的BiFPN模块.
- 实验是在植物表型化 (CVPPP) 叶片细分挑战中的计算机视觉问题中的五个数据集上进行的.
主要成果:
- 标准YOLOv8-seg模型在叶片细分任务上表现良好.
- 整合Ghost和BiFPN模块显著提高了细分性能.
- 拟议的修改后的YOLOv8-seg方法在CVPPP叶片细分挑战数据集上获得了86.4%的最高分数.
结论:
- 增强的YOLOv8-seg型号,包括Ghost和BiFPN模块,为植物叶片细分提供卓越的性能.
- 这项技术在推进精准农业和植物表型研究方面具有重大潜力.
- 这些发现表明,建筑修改可以显著提高基于计算机视觉的农业应用程序的准确性.
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