Journal of Optoelectronics · Laser, Volume. 34, Issue 10, 1075(2023)

A deep learning-based method for grading the grain size of steel metallographic images

WANG Sen1, GUO Rong1, HU Haijun2、*, ZHANG Yu3, and LI Xiufeng4
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
  • 4[in Chinese]
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    References(6)

    [2] [2] ORTEGON J,LEDESMA-ALONSO R,BARBOSA R,et al.Material phase classification by means of support vector machines[J].Computational Materials Science,2018,148:336-342.

    [3] [3] TSUTSUI K,TERASAKI H,MAEMURA T,et al.Microstructural diagram for steel based on crystallography with machine learning[J].Computational Materials Science,2019,159:403-411.

    [4] [4] GAI X,YE P,WANG J,et al.Research on defect detection method for steel metal surface based on deep learning[C]//2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC),June 12-14,2020,Chongqing,China.New York:IEEE,2020:637-641.

    [5] [5] DECOST B,LEI B,FRANCIS T,et al.High throughput quantitative metallography for complex microstructures using deep learning:a case study in ultrahigh carbon steel[J].Microsc Microanal,2019,25(1):21-29.

    [6] [6] LONG J,SHELHAMER E,DARRELL T,et al.Fully convolutional networks for semantic segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(4):640-651.

    [10] [10] AZIMI S M,BRITZ D,ENGSTLER M,et al.Advanced steel microstructural classification by deep learning methods[J].Scientific Reports,2018,8(1):2128.

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    WANG Sen, GUO Rong, HU Haijun, ZHANG Yu, LI Xiufeng. A deep learning-based method for grading the grain size of steel metallographic images[J]. Journal of Optoelectronics · Laser, 2023, 34(10): 1075

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    Paper Information

    Received: Sep. 21, 2022

    Accepted: --

    Published Online: Sep. 25, 2024

    The Author Email: HU Haijun (1264180391@qq.com)

    DOI:10.16136/j.joel.2023.10.0650

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