基于多粒度多阶段特征学习的叶片分类和分级方法  

A Leaf Classification and Ranking Method Based on Multi-granularity Multi-stage Feature Learning

作　　者：陈亚杰 刘松岳 王潇 CHEN Yajie;LIU Songyue;WANG Xiao(School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094;Bijie Cigarette Factory of China Tobacco Guizhou Industry Co.,Ltd.,Bijie 551799)

机构地区：[1]南京理工大学计算机科学与工程学院,南京210094 [2]贵州中烟工业有限责任公司毕节卷烟厂,毕节551799

出　　处：《计算机与数字工程》2024年第7期2150-2154,共5页Computer & Digital Engineering

基　　金：国家自然科学基金项目(编号:61703209)资助。

摘　　要：论文提出一种新的植物叶片分类以及分级网络模型MGMS(multi-granularity and multi-stage network),该模型关注叶片的多粒度信息,并将多层级粒度特征进行有效融合。模型骨架由通用特征提取网络Resnet50构成,在不同阶段计算提取出特征,并将这些特征拼接,完成多粒度特征提取。此外,还使用了中心差分卷积模块,使模型可以关注图像中更具区分度的局部区域,提取出的特征更具区分性。在训练时采用多阶段训练方式,通过计算每一步提取的特征(包括拼接特征)得到的预测标签与真实标签的损失函数,实现由浅层特征到深层特征的学习,将triplet loss用于模型训练,通过减小anchor与正样本的欧式距离,增大anchor与负样本欧式距离优化目标。该方法在Flavia leaf和Swedish leaf两个公开的叶片分类数据集上分别达到99.3%和99.9%的分类准确率,其中在Swedish leaf数据集上达到了目前最高准确率,在Flavia leaf上与当前最高准确率的方法相当,且在构建的烟叶分级数据集上也达到目前最高的71.2%的分级准确率。This article proposes a new plant leaf classification and grading network model MGMS(Multi Granularity and Multi Stage network),which focuses on the multi granularity information of leaves and effectively integrates multi-level granularity fea⁃tures.Specifically,the model skeleton is composed of the general feature extraction network Resnet50,which calculates and ex⁃tracts features at different stages,and concatenates these features to complete multi-granularity feature extraction.In addition,a central differential convolution module is used to enable the model to focus on more discriminative local regions in the image and ex⁃tract more distinctive features.During training,a multi-stage training approach is adopted to achieve learning from shallow features to deep features by calculating the loss function between the predicted labels obtained from each extracted feature(including concat⁃enated features)and the true labels.The article uses triplet loss for model training and increases the optimization objective of the Eu⁃clidean distance between anchors and negative samples by reducing the Euclidean distance between anchors and positive samples.This method achieves classification accuracies of 99.3%and 99.9%on two publicly available leaf classification datasets,Flavia Leaf and Swedish Leaf respectively.The highest accuracy is achieved on the Swedish Leaf dataset,which is comparable to the cur⁃rent highest accuracy method on Flavia Leaf.Additionally,the highest classification accuracy of 71.2%is achieved on the construct⁃ed tobacco grading dataset.

关 键 词：叶片分类 多粒度融合 多步骤训练 中心差分卷积 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]