谷物网络:基于改进的YOLOv7建模,有效检测和计数小麦粒
Xin Wang1, Changchun Li2, Chenyi Zhao1
1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, 454000, China.
Plant methods
|March 25, 2025
概括
这项研究介绍了GrainNet,这是一种用于准确计数小麦粒的新型模型,即使是附着和复杂的成像. 谷物网显著提高了检测速度和准确性,以进行有效的种子测试.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 种子测试对于提高作物产量至关重要.
- 精确的小麦粒计数受到谷物粘附和复杂的成像环境的阻碍.
- 现有的方法对于各种种子测试场景缺乏普遍适用性.
研究的目的:
- 开发一个快速而准确的小麦谷物计数模型,适用于具有挑战性的条件.
- 提高自动化种子检测方法的普遍适用性.
主要方法:
- 使用各种成像条件和数据增强,创建了一个全面的小麦谷物数据集.
- 提出了一个新的小麦谷物检测和计数模型,GrainNet.
- 谷物网络包含一个高效的多尺度注意力 (EMA) 机制和一个ASF收集和分发 (ASF-GD) 模块,优化YOLOv7.
主要成果:
- 在各种场景中,GrainNet在Faster R-CNN,YOLOv5,YOLOv7和YOLOv8上表现出优越的性能.
- 实现了平均平均精度为93.15%,F1得分为0.946,检测速度为29.10 FPS.
- 达到94.47%的计数精度,确定系数为0.93,平均绝对误差为5.97.
结论:
- 谷物网可以在现实场景中准确和快速量化小麦粒.
- 该模型是有效的小麦种子检查的宝贵工具.
- 为推进自动化种子测试技术提供参考.
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