通过CNN和双向部门搜索检测和重建激情果枝
Jiangchuan Bao1, Guo Li1, Haolan Mo2
1Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin 541004, China.
Plant phenomics (Washington, D.C.)
|September 11, 2023
概括
本研究介绍了一种改进的基于面具区域的卷积神经网络 (Mask R-CNN),用于在复杂的果园环境中准确地检测和重建分支. 该方法通过克服密集的树叶和复杂的分支结构所带来的挑战,增强了机器人收获和植物表型.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 植物科学 植物科学
背景情况:
- 准确的分支检测和重建对于农业机器人和植物表型定型至关重要.
- 复杂的果园背景和类似葡萄树的分支结构对现有方法构成重大挑战.
研究的目的:
- 开发一种先进的方法,在具有挑战性的果园环境中对植物树枝进行细分和重建.
- 为了提高分支检测的准确性和效率,用于诸如机器人收获等应用.
主要方法:
- 使用基于Mask区域的卷积神经网络 (Mask R-CNN) 架构,增强了用于分支细分的可变形卷积.
- 开发了一种基于生长姿势的适应性分支重建的双向部门搜索算法.
主要成果:
- 改进的Mask R-CNN实现了64.30%的精度,76.51%的回忆率和69.88%的F1分数来检测激情果树枝,每个图像的平均处理时间为0.75秒.
- 分支重建的准确性达到88.83%,mIoU为83.44%,平均重建时间为每张图像0.38s.
结论:
- 拟议的面具R-CNN具有可变形卷积和自适应重建算法,有效地应对复杂果园背景中的挑战.
- 这种方法显示了准确和高效的植物分支检测和重建的希望,支持农业自动化方面的进步.
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