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TriPerceptNet:一种轻量级的多尺度增强的YOLOv11模型,用于在复杂的田间环境中准确检测水疾病

Xin Zhang¹, Linjing Wei¹, Ruqiang Yang¹

¹College of Information Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China.

Frontiers in plant science

|September 22, 2025

概括

一个新的轻量级大米病检测模型,EDGE-MSE-YOLOv11,通过三模轻量级感知机制 (TMLPM) 提高了准确性和效率. 这种模型提高了在复杂的野外条件下检测植物疾病的精度和回忆力.

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:

准确高效地检测米病对粮食安全至关重要.
现有的模型在复杂的现场条件下与多个规模和小疾病目标作斗争.
实时农业应用需要轻型模型.

研究的目的:

提出EDGE-MSE-YOLOv11,一种新的轻量级大米病检测模型.
加强对多个规模和小疾病目标的检测.
为了提高模型的解释性和计算效率.

主要方法:

三模块轻量感知机制 (TMLPM) 的开发,集成多尺度特征融合 (C3K2 MSEIE),注意引导特征精细化 (SimAM) 和高效的空间下采样 (ADown).
在TMLPM中实现协作功能交互,以提高可解释性和效率.
对基线YOLOv11n模型进行比较实验.

主要成果:

与基线相比,EDGE-MSE-YOLOv11实现了精度 (89.2%),回忆 (86.4%),mAP@0.5 (92.6%) 和mAP@0.5:0.95 (70.3%) 的改进.
该模型将参数数量减少了0.69M,计算成本减少了0.3 GFLOP.
保持了111.6 FPS的高推断速度,在检测小而密集的病变方面表现出高精度和效率.

结论:

EDGE-MSE-YOLOv11是有效的准确和高效的病检测,特别是对于小和密集的病变.
TMLPM 增强了模型处理复杂现场条件的能力.
未来的工作包括跨领域的概括和优化,以便在智能农业系统中部署.

关键词:

ADown 是自己的.C3K2 MSEIEIEIEIE 在线阅读在 GradCAM++++ 中使用.这就是SIMAMAM.TMLPM TMLPM TMLPM TMLPM TMLPM TMLPM TMLPM TMLPM TML 这就是YOLOv11的意义.这是一个轻量级的CNN CNN.检测大米疾病检测大米疾病

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