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A Pix2pixGAN-Based Method For Carbide Segmentation In GCr15 Steel.

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Title: A Pix2pixGAN-Based Method For Carbide Segmentation In GCr15 Steel.
Authors: Wang, Jiangang1 wm094212@163.com, Lian, Xiaolong2 13102731601@163.com, Han, Borui2 1491453212@qq.com, Sun, Yixiao3 robinsunyixiao@163.com, Ju, Dongying1 dyju.sitec@gmail.com, Wu, Yanzhao4 yzw@hebust.edu.cn, Zhang, Xin5 zhxin5210@163.com, Yang, Liyong6 sgyangliyong@hbisco.com
Source: IAENG International Journal of Computer Science. Jun2026, Vol. 53 Issue 6, p2264-2271. 8p.
Subjects: Generative adversarial networks, Carbides, Artificial neural networks, Metallography, Deep learning, Data augmentation, Steel
Abstract: Metallographic analysis plays a key role in modern materials science, as the morphology and distribution of carbides after quenching strongly influence the performance and its subsequent heat treatment. In this study, a dataset of quenched alloy carbides was established, and a carbide segmentation model based on pix2pix Generative Adversarial Network (pix2pixGAN) was proposed. The model integrates a new feature enhancement module, ECA-NLA, which combines Efficient Channel Attention mechanism with non-local spatial attention module to strengthen feature extraction, enhance channel perception, and enable adaptive feature weighting. In addition, the Inception v1 module was incorporated to enable multi-scale feature extraction and reduce pixel-level information loss, while depthwise separable convolutions were used to improve the generator's representational capacity and efficiency, resulting in notable performance gains. Experimental results demonstrate the effectiveness of the improved pix2pixGAN model, achieving an Intersection over Union of 83.8% on the proposed dataset, which is 5.1% higher than that of the baseline pix2pixGAN model. By leveraging recent advances in deep learning architectures and data optimization techniques, this study improves the automation and accuracy of alloy carbide evaluation and provides a promising solution for automated metallographic analysis. [ABSTRACT FROM AUTHOR]
Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Label: Title
  Group: Ti
  Data: A Pix2pixGAN-Based Method For Carbide Segmentation In GCr15 Steel.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Jiangang%22">Wang, Jiangang</searchLink><relatesTo>1</relatesTo><i> wm094212@163.com</i><br /><searchLink fieldCode="AR" term="%22Lian%2C+Xiaolong%22">Lian, Xiaolong</searchLink><relatesTo>2</relatesTo><i> 13102731601@163.com</i><br /><searchLink fieldCode="AR" term="%22Han%2C+Borui%22">Han, Borui</searchLink><relatesTo>2</relatesTo><i> 1491453212@qq.com</i><br /><searchLink fieldCode="AR" term="%22Sun%2C+Yixiao%22">Sun, Yixiao</searchLink><relatesTo>3</relatesTo><i> robinsunyixiao@163.com</i><br /><searchLink fieldCode="AR" term="%22Ju%2C+Dongying%22">Ju, Dongying</searchLink><relatesTo>1</relatesTo><i> dyju.sitec@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Wu%2C+Yanzhao%22">Wu, Yanzhao</searchLink><relatesTo>4</relatesTo><i> yzw@hebust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xin%22">Zhang, Xin</searchLink><relatesTo>5</relatesTo><i> zhxin5210@163.com</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Liyong%22">Yang, Liyong</searchLink><relatesTo>6</relatesTo><i> sgyangliyong@hbisco.com</i>
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  Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Jun2026, Vol. 53 Issue 6, p2264-2271. 8p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Generative+adversarial+networks%22">Generative adversarial networks</searchLink><br /><searchLink fieldCode="DE" term="%22Carbides%22">Carbides</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Metallography%22">Metallography</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Steel%22">Steel</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Metallographic analysis plays a key role in modern materials science, as the morphology and distribution of carbides after quenching strongly influence the performance and its subsequent heat treatment. In this study, a dataset of quenched alloy carbides was established, and a carbide segmentation model based on pix2pix Generative Adversarial Network (pix2pixGAN) was proposed. The model integrates a new feature enhancement module, ECA-NLA, which combines Efficient Channel Attention mechanism with non-local spatial attention module to strengthen feature extraction, enhance channel perception, and enable adaptive feature weighting. In addition, the Inception v1 module was incorporated to enable multi-scale feature extraction and reduce pixel-level information loss, while depthwise separable convolutions were used to improve the generator's representational capacity and efficiency, resulting in notable performance gains. Experimental results demonstrate the effectiveness of the improved pix2pixGAN model, achieving an Intersection over Union of 83.8% on the proposed dataset, which is 5.1% higher than that of the baseline pix2pixGAN model. By leveraging recent advances in deep learning architectures and data optimization techniques, this study improves the automation and accuracy of alloy carbide evaluation and provides a promising solution for automated metallographic analysis. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: 2264
    Subjects:
      – SubjectFull: Generative adversarial networks
        Type: general
      – SubjectFull: Carbides
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Metallography
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Data augmentation
        Type: general
      – SubjectFull: Steel
        Type: general
    Titles:
      – TitleFull: A Pix2pixGAN-Based Method For Carbide Segmentation In GCr15 Steel.
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            NameFull: Wang, Jiangang
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            NameFull: Lian, Xiaolong
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            NameFull: Han, Borui
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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