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A deep learning-based method for segmentation and quantitative characterization of microstructures in weathering steel from sequential scanning electron microscope images

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Journal of Iron and Steel Research International 2022年第5期29卷 836-845页

作者： Bing Han Wei-hao Wan Dan-dan Sun Cai-chang Dong Lei Zhao Hai-zhou Wang Beijing Advanced Innovation Center for Materials Genome Engineering Central Iron&Steel Research InstituteBeijing100081China Qingdao NCS Testing&Corrosion Protection Technology Co. Ltd.Qingdao266071ShandongChina Beijing Key Laboratory of Metal Materials Characterization Central Iron&Steel Research InstituteBeijing100081China

Microstructural classification is typically done manually by human experts,which gives rise to uncertainties due to subjectivity 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 *** 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%,*** time required for batch processing by MIPAR software involving thresholding segmentation,morphological processing,and small area deletion was 1.05 s for a single *** comparison,our system required only 0.102 s,which is ten times faster than the commercial *** quantification results were extracted from large volumes of sequential image data(150 mm^(2),62,216 images,1024×1024 pixels),which ensure comprehensive *** information,such as three-dimensional density distribution and the frequency of the minimum spatial distance of inclusions on the sample surface of 150 mm^(2),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 *** will be useful for understanding properties or behaviors of weathering steel,and reducing the resort to physical testing.

关键词： Deep learning High-throughput Microstructure sequential image Rapid acquisition Quantitative characterization Segmentation

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Feature-based sequential partial vision measurement method for large scale machine parts

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Journal of Southeast University(English Edition) 2007年第4期23卷 550-555页

作者：张志胜何博侠戴敏史金飞东南大学机械工程学院南京211189

To realize the high-precision vision measurement for large scale machine parts, a new vision measurement method based on dimension features of sequential partial images is proposed. Instead of mosaicking the partial images, extracting the dimension features of the sequential partial images and deriving the part size according to the relationships between the sequential images is a novel method to realize the high- precision and fast measurement of machine parts. To overcome the corresponding problems arising from the relative rotation between two sequential partial images, a rectifying method based on texture features is put forward to effectively improve the processing speed. Finally, a case study is provided to demonstrate the analysis procedure and the effectiveness of the proposed method. The experiments show that the relative error is less than 0. 012% using the sequential image measurement method to gauge large scale straight-edge parts. The measurement precision meets the needs of precise measurement for sheet metal parts.

关键词： vision measurement sequential image texture feature feature matching

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