一个基于改进的YOLOv11x的田间大米恐慌检测模型
Yuzhu Luo1, Xinyu Li1, Bing Bai1
1Institute of Information, Liaoning Academy of Agricultural Sciences, Shenyang, China.
Frontiers in plant science
|September 18, 2025
概括
这项研究引入了一种改进的YOLOv11x模型,用于使用无人机图像准确检测米粉. 改进后的模型显著提高了检测性能,这对于全球粮食安全和精确的产量估计至关重要.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
背景情况:
- 米是全球的主食,需要准确的产量预测,以确保粮食安全.
- 手动计数米饼是低效和有偏见的.
- 基于无人机的恐慌探测面临诸如密集分布,尺度变化和遮等挑战.
研究的目的:
- 使用改进的YOLOv11x架构开发一个先进的米粉检测模型.
- 增强特征表示,空间依赖性捕获和多尺度融合,以提高检测准确度.
- 提供可靠的解决方案,用于在田间智能地检测米粉,并精确估计产量.
主要方法:
- 开发了一个改进的You Only Look Once版本11x (YOLOv11x) 架构.
- 主要的改进包括双级路由注意力 (BRA),基于变压器的检测头 (TransHead) 和选择性内核 (SK) 注意力.
- 整合了多层次的功能融合架构,以提高多规模的适应性.
主要成果:
- 改进的模型实现了89.4%的mAP@0.5,比基线YOLOv11x.x增加了3%.
- 获得了87.3%的精度和84.1%的F1得分,优于YOLOv8和更快的R-CNN.
- 盘点计数测试显示,R2 = 0.85,RMSE = 2.33 和 rRMSE = 0.13 具有强烈的匹配.
结论:
- 拟议的模型提供了一种可靠的解决方案,用于使用无人机图像进行智能在野外的米团检测.
- 这一进步对于精确的产量估计具有重要意义,并有助于粮食安全.
- 该模型与现有的主流算法相比,显示出更高的性能.
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