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Parallel Algorithms to Detect and Classify Defects in Surface Steel Strips

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Advances in Artificial Intelligence and Applied Cognitive Computing

Abstract

In steel industry, automatic defects inspection and classification is of great importance to improve the quality. This chapter proposes and develops parallel algorithms using CUDA to improve the required computing time to detect defects in surface steel strips. The algorithm divides steel images into non-overlapped region of interest (ROI) and employs the summed area table to improve the required time to extract statistical features per (block) ROI. The computation time of the proposed parallel algorithm excels the sequential one. Support vector machine classifier has been used to classify patches, scratches, and scale defects. The experimental results indicate significant improvements and 1.6 speed up.

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Author information

Authors and Affiliations

  1. School of Computing, Southern Illinois University, Carbondale, IL, USA

    Khaled R. Ahmed

  2. King Faisal University, Hofuf, Saudi Arabia

    Majed Al-Saeed & Maryam I. Al-Jumah

Authors
  1. Khaled R. Ahmed
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  2. Majed Al-Saeed
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  3. Maryam I. Al-Jumah
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Corresponding author

Correspondence to Khaled R. Ahmed .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Georgia, Athens, GA, USA

    Hamid R. Arabnia

  2. Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada

    Ken Ferens

  3. Business Administration, University of Oviedo, Oviedo, Asturias, Spain

    David de la Fuente

  4. Institute of Informatics Problems, The Russian Academy of Sciences, Moscow, Russia

    Elena B. Kozerenko

  5. Technology and Information systems, Universidad de Castilla La Mancha, Ciudad Real, Ciudad Real, Spain

    José Angel Olivas Varela

  6. Facultad de Informática - CIC PBA, Universidad Nacional de La Plata, La Plata, Argentina

    Fernando G. Tinetti

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Ahmed, K.R., Al-Saeed, M., Al-Jumah, M.I. (2021). Parallel Algorithms to Detect and Classify Defects in Surface Steel Strips. In: Arabnia, H.R., Ferens, K., de la Fuente, D., Kozerenko, E.B., Olivas Varela, J.A., Tinetti, F.G. (eds) Advances in Artificial Intelligence and Applied Cognitive Computing. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-70296-0_40

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  • DOI: https://doi.org/10.1007/978-3-030-70296-0_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70295-3

  • Online ISBN: 978-3-030-70296-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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