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基于TCLE-YOLO模型的米粒检测和计数方法

Yu Zou1, Zefeng Tian2, Jiawen Cao2

  • 1Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
概括
此摘要是机器生成的。

一个新的深度学习模型,TCLE-YOLO,准确地检测和计算米粒,这对于产量估计和育种至关重要. 这种方法通过克服小型粘合粒的挑战,提高了千粒重量测量.

关键词:
这是YOLOv5的.协调注意力模块的协调检测和计数大米粒的检测和计数转换 转换 转换 转换 转换

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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:

  • 准确的米产估计依赖于千粒重量,需要精确的米粒检测和计数.
  • 挑战包括小粒度,高相似性和粘附性,阻碍了传统的测量方法.

研究的目的:

  • 开发一种先进的深度学习模型,用于准确检测和计数大米粒.
  • 为了提高千粒重量测量的可靠性,用于米育种和种植.

主要方法:

  • 使用YOLOv5作为骨干设计了一个TCLE-YOLO模型,其中包含一个坐标注意 (CA) 模块,用于增强小目标特征表示.
  • 增加了针对小目标的专用检测头,使用低级,高分辨率的特征地图.
  • 将变压器编码器集成到部模块中,以扩大接收场并改善特征提取,特别是对于粘合粒.
  • 使用EIoU损失来进一步完善检测准确性.

主要成果:

  • 在自建数据集上,TCLE-YOLO模型实现了高性能,精度,回忆和mAP@0.5分别达到99.20%,99.10%和99.20%.
  • 该模型与几种最先进的模型相比,显示出更高的检测性能.
  • 对小粒和粘合剂颗粒的增强敏感性得到证实.

结论:

  • TCLE-YOLO模型为米粒识别和计数提供了有效的解决方案.
  • 这种方法为准确的千粒重量测量和高效的米育种评估提供了宝贵的支持.