Automatic segmentation of lamellar eutectoids in iron oxide scales using optimized U-net network

被引：0

作者：

Wang, Hao ^{[1
]}

Cao, Guangming ^{[1
]}

Zhao, Wencong ^{[1
]}

Wu, Siwei ^{[1
]}

Li, Zhifeng ^{[1
]}

Liu, Zhenyu ^{[1
]}

机构：

[1] Northeastern Univ, State Key Lab Rolling & Automat, Shenyang, Liaoning, Peoples R China

来源：

VACUUM | 2024年 / 222卷

关键词：

Eutectoids; Machine learning; Phase transformation; SENet; U-net;

D O I：

10.1016/j.vacuum.2024.113071

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The variation in lamellar spacing of eutectoids in iron oxide affects the surface quality of the hot-rolled strip and its subsequent deep processing. In order to investigate the lamellar spacing numerically, an accurate segmentation of Fe and Fe3O4 phases is required in the structural picture. In this paper, a dataset was generated for identifying Fe and Fe3O4 in eutectoids. Also, a squeeze-and-excitation network module was integrated for improving the accuracy in image segmentation. The obtained experimental results show that the PA and mIoU could reach 96.83% and 94.49% in optimized U-net, indicating its excellent segmentation capability compared with traditional image segmentation networks. Using this method, fast and accurate extraction of the characteristic information about the picture can be realized, which would help in investigating the phase transition behavior of the eutectoid transformation in the Fe-O system.

引用

页数：4

共 14 条

[1] Advanced Steel Microstructural Classification by Deep Learning Methods [J].

Azimi, Seyed Majid ;

Britz, Dominik ;

Engstler, Michael ;

Fritz, Mario ;

Muecklich, Frank .

SCIENTIFIC REPORTS, 2018, 8

[2]

Chen LC, 2017, Arxiv, DOI [arXiv:1706.05587, 10.48550/arXiv.1706.05587]

[3] Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation [J].

Chen, Liang-Chieh ;

Zhu, Yukun ;

Papandreou, George ;

Schroff, Florian ;

Adam, Hartwig .

COMPUTER VISION - ECCV 2018, PT VII, 2018, 11211 :833-851

[4] DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs [J].

Chen, Liang-Chieh ;

Papandreou, George ;

Kokkinos, Iasonas ;

Murphy, Kevin ;

Yuille, Alan L. .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2018, 40 (04) :834-848

[5] Review of the high-temperature oxidation of iron and carbon steels in air or oxygen [J].

Chen, RY ;

Yuen, WYD .

OXIDATION OF METALS, 2003, 59 (5-6) :433-468

[6] PGA-Net: Pyramid Feature Fusion and Global Context Attention Network for Automated Surface Defect Detection [J].

Dong, Hongwen ;

Song, Kechen ;

He, Yu ;

Xu, Jing ;

Yan, Yunhui ;

Meng, Qinggang .

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2020, 16 (12) :7448-7458

[7] Stress measurement in the iron oxide scale formed on pure Fe during isothermal transformation by in situ high-temperature X-ray diffraction [J].

Hayashi, Shigenari ;

Yamanouchi, Yurika ;

Hayashi, Kosuke ;

Hidaka, Yasuyoshi ;

Sato, Masugu .

CORROSION SCIENCE, 2021, 187

[8] The Mechanism of Phase Transformation in Thermally-Grown FeO Scale Formed on Pure-Fe in Air [J].

Hayashi, Shigenari ;

Mizumoto, Kouhei ;

Yoneda, Suzue ;

Kondo, Yasumitsu ;

Tanei, Hiroshi ;

Ukai, Shigeharu .

OXIDATION OF METALS, 2014, 81 (3-4) :357-371

[9]

Hu J, 2018, PROC CVPR IEEE, P7132, DOI [10.1109/TPAMI.2019.2913372, 10.1109/CVPR.2018.00745]

[10] Grain boundary detection and second phase segmentation based on multi-task learning and generative adversarial network [J].

Li, Mingchun ;

Chen, Dali ;

Liu, Shixin ;

Liu, Fang .

MEASUREMENT, 2020, 162

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