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Investigation into Defect Image Segmentation Algorithms for Galvanized Steel Sheets Under Texture Background

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Proceedings of TEPEN 2022 (TEPEN 2022)

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 129))

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Abstract

Galvanized steel sheets have been widely used due to their excellent corrosion resistance and sound formability, but their surface defects can severely affect their performance, so it is of immense significance to identify them effectively and accurately. This paper selected the images of surface defects of galvanized steel sheets as the research objects, investigated the segmentation of surface defects under complex texture backgrounds, and offered an optimized two-dimensional asymmetric Tsallis cross entropy image segmentation algorithm based on Chaotic Bee Colony Algorithm. On the basis of Tsallis cross entropy threshold segmentation algorithm, a simpler expression was adopted to define the asymmetric Tsallis cross entropy in order to reduce its calculation complexity; chaotic algorithm and Artificial Bee Colony Algorithm were combined to construct Chaotic Bee Colony Algorithm, so that the optimal threshold of Tsallis entropy could be searched quickly. The experimental results showed that compared with other commonly used threshold segmentation algorithms, the algorithm proposed by this paper could rapidly and effectively segment defect targets, a more suitable method of detecting surface defects for factories with a rapid production pace.

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Acknowledgements

This research was supported by National Key Research and Development Program of China (No. 2020YFB1713203); Beijing Key Laboratory of Measurement & Control of Mechanical and Electrical System Technology, Beijing Information Science & Technology University (No. KF20202223202).

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Authors and Affiliations

  1. Key Laboratory of Measurement and Control of Mechanical and Electrical System Technology, Beijing Information Science and Technology University, Beijing, 100101, China

    Guoxin Wu & Xiaoli Xu

  2. Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology and Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua, 321004, China

    Rui Pan & Yuda Chen

  3. College of Engineering, Zhejiang Normal University, Jinhua, 321004, China

    Rui Pan & Yuda Chen

Authors
  1. Rui Pan
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  2. Yuda Chen
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  3. Guoxin Wu
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  4. Xiaoli Xu
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Corresponding author

Correspondence to Guoxin Wu .

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Editors and Affiliations

  1. School of Mechanical Engineering, Hebei University of Technology, Tianjin, China

    Hao Zhang

  2. Beijing Information Science & Technology University, Beijing, China

    Yongjian Ji

  3. Inner Mongolia University of Science and Technology, Baotou, Nei Mongol, China

    Tongtong Liu

  4. University of Huddersfield, Huddersfield, UK

    Xiuquan Sun

  5. University of Huddersfield, Huddersfield, UK

    Andrew David Ball

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Pan, R., Chen, Y., Wu, G., Xu, X. (2023). Investigation into Defect Image Segmentation Algorithms for Galvanized Steel Sheets Under Texture Background. In: Zhang, H., Ji, Y., Liu, T., Sun, X., Ball, A.D. (eds) Proceedings of TEPEN 2022. TEPEN 2022. Mechanisms and Machine Science, vol 129. Springer, Cham. https://doi.org/10.1007/978-3-031-26193-0_56

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  • DOI: https://doi.org/10.1007/978-3-031-26193-0_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26192-3

  • Online ISBN: 978-3-031-26193-0

  • eBook Packages: EngineeringEngineering (R0)

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