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WheatLFANet:通过高实时全球回归网络在田间检测和计数小麦头

Jianxiong Ye¹, Zhenghong Yu², Yangxu Wang¹

¹College of Robotics, Guangdong Polytechnic of Science and Technology, Zhuhai, Guangdong, China.

|October 4, 2023

概括

WheatLFANet是一个新的轻量级神经网络,用于高效地检测小麦头部. 它在低端设备上实现了高精度,在作物管理的速度和实用性方面超过了现有的方法.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
植物表型化植物表型化

背景情况:

小麦头部检测对于作物管理,产量预测和表型分析至关重要.
自动化植物表型依赖于精确的小麦头部识别.
当前的方法往往需要高性能计算,在资源有限的环境中限制实际应用.

研究的目的:

开发一个轻量级和高实时的神经网络用于小麦头部检测.
在低端设备上实现高效的小麦头部检测.

主要方法:

开发WheatLFANet,这是一个基于TasselLFANet的轻量级神经网络.
使用了紧的编码器解码器结构和多维信息映射融合.
根据全球小麦头检测数据集进行评估.

主要成果:

小麦LFANet的平均精度 (AP) 为0.900,R2值为0.949.
与最先进的方法相比,在准确性和实用性方面表现出卓越的性能.
与现有方法相比,实现了数量级的速度增加,在低端设备上运行高效.

结论:

惠特LFANet提供了卓越的概括能力和实时检测能力.
该研究验证了在资源有限的设备上轻量级小麦头部检测的可行性.
突出了用于实际农业应用的简单而强大的神经网络设计的有效性.

关键词:

检测和计数的检测和计数高效实时的高效实时时间.神经网络的神经网络的神经网络实用性实用性的小麦头的小麦头.

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