对口腔孔实例细分的比较分析:面具R-CNN与YOLOv8在Phenomics上的口腔数据集
Thanh Tuan Thai1,2,3, Ki-Bon Ku4, Anh Tuan Le3,5
1Department of Plant Resources and Environment, Jeju National University, Jeju, Republic of Korea.
Frontiers in plant science
|December 23, 2024
概括
在胃孔孔分析中,YOLOv8的表现优于Mask R-CNN,可以准确测量植物结构. 先进的图像增强技术改善了植物生理学研究的分析.
科学领域:
- 植物科学 植物科学
- 计算机视觉 计算机视觉
- 生物信息学是一种生物信息学.
背景情况:
- 准确的胃孔分析对于了解植物生理学和对环境变化的反应至关重要.
- 实例细分方法为工厂结构的自动化分析提供了潜力.
- 现有的方法面临着低分辨率和不完美的图像数据的挑战.
研究的目的:
- 为了比较Mask R-CNN和YOLOv8对于胃孔实例细分的性能.
- 介绍和利用新的PhenomicsStomata数据集用于胃孔分析.
- 评估高级图像预处理对细分精度的影响.
主要方法:
- 对Mask R-CNN和YOLOv8算法的比较分析.
- 开发和应用PhenomicsStomata数据集.
- 使用Lucy-Richardson算法实现图像增强.
- 基于准确性,精度和回忆指标的评估.
主要成果:
- 与Mask R-CNN相比,YOLOv8在胃孔细分方面表现优越.
- YOLOv8在计算口腔孔径尺寸方面取得了更高的准确性.
- 图像增强技术提高了口腔毛孔分析的质量.
结论:
- YOLOv8是用于胃孔实例细分的高效方法.
- 先进的预处理提高了植物结构分析的可靠性.
- 这些发现为改进植物生理学研究和图像分析技术提供了基础.
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