典型文献
Semantic Segmentation Using DeepLabv3+Model for Fabric Defect Detection
文献摘要：
Currently,numerous automatic fabric defect detection algorithms have been proposed.Traditional machine vision algo-rithms that set separate parameters for different textures and de-fects rely on the manual design of corresponding features to com-plete the detection.To overcome the limitations of traditional algo-rithms,deep learning-based correlative algorithms can extract more complex image features and perform better in image classifi-cation and object detection.A pixel-level defect segmentation methodology using DeepLabv3+,a classical semantic segmenta-tion network,is proposed in this paper.Based on ResNet-18,ResNet-50 and Mobilenetv2,three DeepLabv3+networks are con-structed,which are trained and tested from data sets produced by capturing or publicizing images.The experimental results show that the performance of three DeepLabv3+networks is close to one another on the four indicators proposed(Precision,Recall,Fl-score and Accuracy),proving them to achieve defect detection and semantic segmentation,which provide new ideas and techni-cal support for fabric defect detection.
文献关键词：
中图分类号：
作者姓名：
ZHU Runhu;XIN Binjie;DENG Na;FAN Mingzhu
作者机构：
School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China;School of Textile and Fashion Technology,Shanghai University of Engineering Science,Shanghai 201620,China
文献出处：
引用格式：
[1]ZHU Runhu;XIN Binjie;DENG Na;FAN Mingzhu-.Semantic Segmentation Using DeepLabv3+Model for Fabric Defect Detection)[J].武汉大学自然科学学报（英文版）,2022(06):539-549
A类：
DeepLabv3+Model,segmenta,DeepLabv3+networks
B类：
Semantic,Segmentation,Using,Fabric,Defect,Detection,Currently,numerous,automatic,fabric,defect,detection,algorithms,have,been,proposed,Traditional,machine,vision,that,separate,parameters,different,textures,fects,rely,manual,design,corresponding,features,plete,To,overcome,limitations,traditional,deep,learning,correlative,can,extract,more,complex,better,classifi,cation,object,pixel,level,segmentation,methodology,using,classical,semantic,this,paper,Based,ResNet,Mobilenetv2,three,are,con,structed,which,trained,tested,from,data,sets,produced,by,capturing,publicizing,images,experimental,results,show,performance,close,one,another,four,indicators,Precision,Recall,Fl,score,Accuracy,proving,them,achieve,provide,new,ideas,techni,support
AB值：
0.632119
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