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LGWheatNet:一个基于多尺度信息融合的轻量级小麦尖峰检测模型

Zhaomei Qiu¹, Fei Wang¹, Tingting Li¹

¹College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China.

Plants (Basel, Switzerland)

|April 12, 2025

概括

本研究介绍了LGWheatNet,这是一种用于有效检测和计数小麦峰值的新型深度学习模型. 它以显著更少的资源实现了高精度,超过了精准农业的现有方法.

科学领域:

计算机视觉计算机视觉
农业技术农业技术
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

准确检测小麦峰对于高效的作物管理和精准农业至关重要.
现有的方法经常在资源限制和准确性方面扎,特别是在复杂的现场条件下.
需要轻量级,高性能模型来实时监控农业.

研究的目的:

为资源有限的环境开发一个准确和高效的小麦尖峰检测网络.
为了提高小麦的计数准确度,跨越各种生长阶段.
引入适用于农业成像的创新轻量级物体检测方法.

主要方法:

开发了一个新的网络模块SeCUIB,集成MobileNet和ShuffleNet.
拟议的LGWheatNet,其中包括DWDown,SPPF和LightDetect模块.
在多个生长阶段利用定制的小麦数据集.
在大规模图像处理中采用了切片辅助超推理策略.

主要成果:

LGWheatNet实现了高性能:精度 (0.956),回忆 (0.921),mAP50 (0.967),mAP50-95 (0.747) 等等.
经过 1.7M 参数和 5.0 GFLOPs 证明了卓越的资源效率.
在检测准确性和资源使用方面表现优于YOLO,EfficientDet和RetinaNet.
通过切片辅助超推理显著改善了小目标和边缘区域的检测.
在小麦尖计数方面表现出色,特别是在填充和成熟阶段.

结论:

LGWheatNet提供了一种高效可靠的解决方案,用于检测和计数小麦.
拟议的轻量级架构和推断策略适用于资源有限的农业应用.
这项工作推进了用于精密农业的物体检测技术,特别是使用无人机捕获的数据.

关键词:

在LightDetect中检测到光线.计算机视觉计算机视觉计数计数计数计数计数深度学习是一种深度学习.功能融合功能融合功能切片有助于超推理推理.

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Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.