基于改进UperNet的结球甘蓝叶球识别方法  

Identification Method of Kale Leaf Ball Based on Improved UperNet

作　　者：朱轶萍 吴华瑞 郭旺[2,3,4] 吴小燕 ZHU Yiping;WU Huarui;GUO Wang;WU Xiaoyan(School of Computer Science and Communication Engineering,Jiangsu University,Zhenjiang 212013,China;National Engineering Research Center for Information Technology in Agriculture,Beijing 100097,China;Information Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China;Key Laboratory of Digital Village Technology,Ministry of Agriculture and Rural Affairs,Beijing 100097,China)

机构地区：[1]江苏大学计算机科学与通信工程学院,江苏镇江212013 [2]国家农业信息化工程技术研究中心,北京100097 [3]北京市农林科学院信息技术研究中心,北京100097 [4]农业农村部数字乡村技术重点实验室,北京100097

出　　处：《智慧农业（中英文）》2024年第3期128-137,共10页Smart Agriculture

基　　金：“十四五”国家重点研发计划项目(2022YFD1600602);财政部和农业农村部:国家现代农业产业技术体系资助(CARS-23-D07)。

摘　　要：[目的/意义]叶球是结球甘蓝的重要部分,其生长发育对田间管理至关重要。针对叶球分割识别存在大田背景复杂、光照不均匀和叶片纹理相似等问题,提出一种语义分割算法UperNet-ESA,旨在能快速、准确地分割田间场景中结球甘蓝的外叶和叶球,以实现田间结球甘蓝的智能化管理。[方法]首先,采用统一感知解析网络(Unified Perceptual Parsing Network,UperNet)作为高效语义分割框架,将主干网络改为先进的ConvNeXt,使得模型在提升分割精度的同时也能具有较低的模型复杂度;其次,利用高效通道注意力机制(Efficient Channel Attention,ECA)融入特征提取网络的各阶段,进一步捕捉图像的细节信息;最后,通过将特征选择模块(Feature Selection Model,FSM)和特征对齐模块(Feature Alignment Model,FAM)集成到特征金字塔框架中,得到更为精确的目标边界预测结果。[结果和讨论]在自制结球甘蓝图像数据集上进行实验,与目前主流的UNet、PSPNet和DeeplabV3+语义分割模型相比,改进UperNet方法的平均交并比为92.45%,平均像素准确率为94.32%,推理速度为16.6 f/s,能够达到最佳精度-速度平衡效果。[结论]研究成果可为结球甘蓝生长智能化监测提供理论参考,对甘蓝产业发展具有重要的应用前景。[Objective]Kale is an important bulk vegetable crop worldwide,its main growth characteristics are outer leaves and leaf bulbs.The traits of leaf bulb kale are crucial for adjusting water and fertilizer parameters in the field to achieve maximum yield.However,various factors such as soil quality,light exposure,leaf overlap,and shading can affect the growth of in practical field conditions.The similarity in color and texture between leaf bulbs and outer leaves complicates the segmentation process for existing recognition models.In this paper,the segmentation of kale outer leaves and leaf bulbs in complex field background was proposed,using pixel values to determine leaf bulb size for intelligent field management.A semantic segmentation algorithm,UperNet-ESA was proposed to efficiently and accurately segment nodular kale outer leaf and leaf bulb in field scenes using the morphological features of the leaf bulbs and outer leaves of nodular kale to realize the intelligent management of nodular kale in the field.[Methods]The UperNet-ESA semantic segmentation algorithm,which uses the unified perceptual parsing network(UperNet)as an efficient semantic segmentation framework,is more suitable for extracting crop features in complex environments by integrating semantic information across different scales.The backbone network was improved using ConvNeXt,which is responsible for feature extraction in the model.The similarity between kale leaf bulbs and outer leaves,along with issues of leaf overlap affecting accurate target contour localization,posed challenges for the baseline network,leading to low accuracy.ConvNeXt effectively combines the strengths of convolutional neural networks(CNN)and Transformers,using design principles from Swin Transformer and building upon ResNet50 to create a highly effective network structure.The simplicity of the ConvNeXt design not only enhances segmentation accuracy with minimal model complexity,but also positions it as a top performer among CNN architectures.In this study,the ConvNeXt-B vers

关 键 词：结球甘蓝 语义分割 叶球识别 注意力机制 特征选择 特征对齐 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]