典型文献
A deep learning-based method for segmentation and quantitative characterization of microstructures in weathering steel from sequential scanning electron microscope images
文献摘要：
Microstructural classification is typically done manually by human experts,which gives rise to uncertainties due to subjec-tivity and reduces the overall efficiency.A high-throughput characterization is proposed based on deep learning,rapid acquisition technology,and mathematical statistics for the recognition,segmentation,and quantification of microstructure in weathering steel.The segmentation results showed that this method was accurate and efficient,and the segmentation of inclusions and pearlite phase achieved accuracy of 89.95％and 90.86％,respectively.The time required for batch processing by MIPAR software involving thresholding segmentation,morphological processing,and small area deletion was 1.05 s for a single image.By comparison,our system required only 0.102 s,which is ten times faster than the commercial software.The quantification results were extracted from large volumes of sequential image data(150 mm2,62,216 images,1024×1024 pixels),which ensure comprehensive statistics.Microstructure information,such as three-dimensional density distribution and the frequency of the minimum spatial distance of inclusions on the sample surface of 150 mm2,were quantified by extracting the coordinates and sizes of individual features.A refined characterization method for two-dimensional structures and spatial information that is unattainable when performing manually or with software is provided.That will be useful for understanding properties or behaviors of weathering steel,and reducing the resort to physical testing.
文献关键词：
中图分类号：
作者姓名：
Bing Han;Wei-hao Wan;Dan-dan Sun;Cai-chang Dong;Lei Zhao;Hai-zhou Wang
作者机构：
Beijing Advanced Innovation Center for Materials Genome Engineering,Central Iron&Steel Research Institute,Beijing 100081,China;Qingdao NCS Testing&Corrosion Protection Technology Co.,Ltd.,Qingdao 266071,Shandong,China;Beijing Key Laboratory of Metal Materials Characterization,Central Iron&Steel Research Institute,Beijing 100081,China
文献出处：
引用格式：
[1]Bing Han;Wei-hao Wan;Dan-dan Sun;Cai-chang Dong;Lei Zhao;Hai-zhou Wang-.A deep learning-based method for segmentation and quantitative characterization of microstructures in weathering steel from sequential scanning electron microscope images)[J].钢铁研究学报（英文版）,2022(05):836-845
A类：
subjec,MIPAR
B类：
deep,learning,method,segmentation,quantitative,characterization,microstructures,weathering,steel,from,sequential,scanning,electron,microscope,images,Microstructural,classification,typically,done,manually,by,human,experts,which,gives,rise,uncertainties,due,tivity,reduces,overall,efficiency,high,throughput,proposed,rapid,acquisition,technology,mathematical,statistics,recognition,quantification,results,showed,that,this,was,accurate,efficient,inclusions,pearlite,phase,achieved,accuracy,respectively,required,batch,processing,software,involving,thresholding,morphological,small,area,deletion,single,By,comparison,our,system,only,ten,times,faster,than,commercial,were,extracted,large,volumes,data,mm2,pixels,ensure,comprehensive,Microstructure,information,such,three,dimensional,density,distribution,frequency,minimum,spatial,distance,sample,surface,quantified,extracting,coordinates,sizes,individual,features,refined,two,unattainable,when,performing,provided,That,will,useful,understanding,properties,behaviors,reducing,resort,physical,testing
AB值：
0.615559
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机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
