空洞卷积结合全局池化的卷积神经网络识别作物幼苗与杂草（附全文PDF）

空洞卷积结合全局池化的卷积神经网络识别作物幼苗与杂草〔附全文PDF〕〔〕:

中文关键词:图像识别农作物幼苗杂草空洞卷积全局池化多尺度特征交融卷积神经网络

基金工程:国家自然科学基金资助工程〔No.31471413〕；江苏高校优势学科建立工程资助工程PAPD〔苏政办发20226号〕；江苏省六大人才顶峰资助工程〔ZBZZ-019〕

作者

单位

孙俊

1.江苏大学电气信息工程学院，镇江212022；

何小飞

1.江苏大学电气信息工程学院，镇江212022；

谭文军

1.江苏大学电气信息工程学院，镇江212022；

武小红

1.江苏大学电气信息工程学院，镇江212022；

沈继锋

1.江苏大学电气信息工程学院，镇江212022；

陆虎

2.江苏大学计算机科学与通信工程学院，镇江212022

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中文摘要:针对传统AlexNet模型参数大、特征尺度单一的问题，该文提出一种空洞卷积与全局池化相结合的多尺度特征交融卷积神经网络识别模型。通过对初始卷积层的卷积核进展膨胀，以增大其感受野而不改变参数计算量，并采用全局池化代替传统的全连接层来减少模型的参数。通过设置不同膨胀系数的初始卷积层卷积核与全局池化层类型，以及设置不同BatchSize，得到8种改良模型，用于训练识别共12种农作物幼苗与杂草，并从建立的模型中选出最优模型。改良后的最优模型与传统AlexNet模型相比，仅经过4次训练迭代，就能到达90%以上的识别准确率，平均测试识别准确率到达98.80%，分类成功指数到达96.84%，模型内存需求减少为4.20MB。实际田间预测野芥与雀麦幼苗的准确率都能到达75%左右，说明该文最优模型对正常情况下的幼苗识别性能较好，但对复杂黑暗背景下的甜菜幼苗准确率为60%，对恶劣背景下的识别性能还有待提升。由于模型使用了更宽的网络构造，增加了特征图的多尺度交融，保持对输入空间变换的不变性，故对正常情况下不同作物幼苗与杂草的识别才能较强。该文改良模型能到达较高的平均识别准确率及分类成功率，可为后续深化探究复杂田间背景下的杂草识别以及杂草与幼苗识别装置的研制打下根底。

SunJun,HeXiaofei,TanWenjun,WuXiaohong,ShenJifeng,LuHu.RecognitionofcropseedlingandweedrecognitionbasedondilatedconvolutionandglobalpoolinginCNN[J].TransactionsoftheChineseSocietyofAgriculturalEngineering(TransactionsoftheCSAE),2022,34(11):159-165.DOI:10.11975/j.issn.1002-6819.2022.11.020

RecognitionofcropseedlingandweedrecognitionbasedondilatedconvolutionandglobalpoolinginCNN

AuthorName

Affiliation

SunJun

1.SchoolofElectricalandInformationEngineeringofJiangsuUniversity,Zhenjiang212022,China

HeXiaofei

1.SchoolofElectricalandInformationEngineeringofJiangsuUniversity,Zhenjiang212022,China

TanWenjun

1.SchoolofElectricalandInformationEngineeringofJiangsuUniversity,Zhenjiang212022,China

WuXiaohong

1.SchoolofElectricalandInformationEngineeringofJiangsuUniversity,Zhenjiang212022,China

ShenJifeng

1.SchoolofElectricalandInformationEngineeringofJiangsuUniversity,Zhenjiang212022,China

LuHu

2.SchoolofputerScienceandmunicationEngineeringofJiangsuUniversity,Zhenjiang212022,China

Keywords:imagerecognitioncropsseedlingweeddilatedconvolutionglobalpoolingmulti-scalefeaturefusionconvolutionneuralnetwork

Abstract:Thedamageofweedsinthefieldtotheseedlingsofcropscanseriouslyaffectthephotosynthesisofplantseedlings.Tosolvethisproblem,andtopreventweedsfromaffectingthegrowthofcropseedlings,itisofgreatsignificancetoaccuratelyidentifycropseedlingsandweeds.Thispaperproposedamodeltoidentifyweedsbasedonimprovedconvolutionalneuralnetwork(CNN).AimingattheproblemsofmanyparametersoftraditionalAlexNetmodelandsinglefeaturescale,thispaperadjustedthenetworkstructureofthetraditionalmodelbybiningdilatedconvolutionandglobalpooling,andextendedthesingleconvolutionkernelintomulti-scaleconvolutionkernelthenfused.Itcanoptimizethetrainingtimeandachievehighprecision.Weregardedeachconvolutionlayerinputtocalculatethebulkmeanandvariance,andthenusedthebatchnormalizationwhilereducingsomeofthecharacteristicsofthelayerfigureatthesametime.Wealsousedtheglobalpoolingtotaketheplaceofthelastfullconnectionlayer.Themodelconsistsof7convolutionlayers,1fusionlayerand4poolinglayers.Inimagepreprocessingphase,inordertopreventthedeviationofthetrainedmodelcausedbytheunbalanceddistributionofsamplenumbers,wehadzoomed,flippedandrotatedtheoriginalpicturesofdatasetrandomlytogettheaugmenteddataset,andused80%ofthepicturesasthetraindatasetandtherestasthetestdataset.Thesepictureswerequantizedto256x256dpiforCNN(convolutionalneuralnetwork)training,andtheoriginaldatasetandaugmenteddatasetwereusedtotrainmodels.Inordertofindtheoptimalexpansioncoefficient,theexpandedconvolutionkernelswithexpansionfactorof2and4wereusedrespectivelyatthefirstlayerofconvolutionkernel.Inaddition,weparedtheglobalaveragepoolingwiththeglobalmaximumpooling.Theresultsshowedthattheglobalaveragepoolingisbetter,whichmeansthattheaveragepoolingcanreducethevarianceoftheestimatedvalueduetothelimitedsizeoftheneighborhood;thebackgroundinformationoftheimagewasretainedmore,itwasmorehelpfulforextractingthekeyfeatures.However,themaximumpoolingpreservesmoretextureinformationandeasilyleadstothelossoftheextracteddeepfeatureinformation.Accordingtovariouskindsofparameters,8sortsofmodelswithdifferentexpansioncoefficientsandpoolingtypesweredesigned.Inordertofurtheroptimizethemodelandimprovetheaveragerecognitionaccuracy,wealsopareddifferentbatchsizessuchas64,128,and256,respectively.Itcanbeseenthatincreasingbatchsizecanincreasememoryutilizationwithinareasonablerangeandtheefficiencyofparallelizationofmatrixmultiplicationisimproved.Thenumberofiterationsrequiredforasingleepoch(fulldataset)isreduced,theprocessingspeedforthesameamountofdatawasaccelerated,andthentheoptimalmodelwasobtained.Fortheperformanceevaluationofthemodel,weusetheaveragerecognitionaccuracyandconfusionmatrixvisualizationtojudge.paredwiththetraditionalAlexNetmodel,theopti