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GFU-Net: A Deep Learning Approach for Automatic Metal Crack Detection

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Neural Computing for Advanced Applications (NCAA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1449))

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Abstract

Crack is a common type of metal indication defects, which brings great hidden dangers to safety of hoisting machinery in use. Automatic metal crack detection methods could be practical in less expensive and high efficiency. In this paper, an encoder-decoder convolutional neural network is proposed, called GFU-Net, which can automatically predict pixel-level crack segmentation by end-to-end method. GFU-Net introduces the guide transformer module on U-Net’s base to strengthen the fusion between corresponding features and applies the Deeply-Supervised Net (DSN), which places features of each convolutional stage under the integrated straight supervision. The experiments show that our work outperforms all other models we test in this article and detects metal cracks from low-contrast images effectively and explicitly.

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Author information

Authors and Affiliations

  1. Jiangsu Special Equipment Safety Supervision and Inspection Institute, Nanjing, 210036, China

    Yanchao Zhang & Xiangdong Li

  2. Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Jingbo Qiu, Xiangping Zhai & Mingqiang Wei

Authors
  1. Yanchao Zhang
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  2. Xiangdong Li
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  3. Jingbo Qiu
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  4. Xiangping Zhai
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  5. Mingqiang Wei
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Corresponding author

Correspondence to Jingbo Qiu .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. Nanfang College of Sun Yat-sen University, Guangzhou, China

    Zhi Yang

  3. Hefei University of Technology, Hefei, China

    Zhao Zhang

  4. Chongqing University, Chongqing, China

    Zhou Wu

  5. South China Normal University, Guangzhou, China

    Tianyong Hao

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Zhang, Y., Li, X., Qiu, J., Zhai, X., Wei, M. (2021). GFU-Net: A Deep Learning Approach for Automatic Metal Crack Detection. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_27

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  • DOI: https://doi.org/10.1007/978-981-16-5188-5_27

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  • Online ISBN: 978-981-16-5188-5

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