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一种改进的基于YOLOv8-seg的方法用于烟草植物的关键部分细分

Yihao Liu¹, Du Chen², Yawei Zhang¹

¹College of Engineering, China Agricultural University, Beijing, China.

Frontiers in plant science

|October 9, 2025

概括

本研究引入了一种增强的YOLOv8-seg模型,用于精确的烟草结构细分,改善自动收获. 该方法使用深度过和架构升级,在具有挑战性的现场条件下实现更高的精度.

科学领域:

计算机视觉计算机视觉
农业机器人农业机器人
机器学习机器学习

背景情况:

烟草植物结构 (茎,树叶) 的准确细分对于自动收获至关重要.
现场条件如复杂的背景,可变的照明和不清晰的边界对现有的细分方法构成重大挑战.
现有的方法难以精确地区分茎和树叶,影响收获效率.

研究的目的:

开发一种增强的实例细分方法,以便在现场环境中准确地细分烟草结构.
提高自动烟草收获系统的准确性和稳定性.
克服当前处理复杂背景和模糊边界的方法的局限性.

主要方法:

提出了一个基于YOLOv8-seg的增强实例细分方法.
来自RGB-D图像的深度信息被用于空间背景过,以提高前景清晰度.
建筑改进,包括混合扩展剩余注意力块 (HDRAB) 和轻量级共享细节增强卷积检测头 (LSDECD),被整合到YOLOv8-seg模型中.
HDRAB的设计是为了改善树枝和茎之间的边界歧视.
LSDECD的开发目的是为了有效地捕捉细粒度的纹理特征.

主要成果:

基于深度的背景过提高了mAP50^bb的7.9%和mAP50^seg的6.3%.
提出的架构改进进一步提高了性能,实现了89.5%的mAP50bb和91.1%的mAP50seg.
增强模型超过了基线YOLOv8-seg的5.2% (mAP50^bb) 和10.0% (mAP50^seg).
与Mask R-CNN和SOLOv2.2相比,该方法显示出更高的细分精度和更低的计算成本.
提出的方法在细分精度方面取得了显著的改进,特别是在具有挑战性的现场条件下.

结论:

提议的增强实例细分方法有效地解决了在现场环境中细分烟草结构的挑战.
基于深度过和新型架构组件的集成显著提高了细分的准确性和稳定性.
该方法由于其高精度和效率,在自动烟草收获系统中具有强大的实际应用潜力.

关键词:

这就是YOLOv8的意义.农业机器人农业机器人自动收获的自动化收获深度学习是一种深度学习.烟草收获烟草收获

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