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Deep Learning for vision systems in Construction 4.0: a systematic review

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Abstract

Recently, the construction industry has been digitizing its production processes, the so-called Construction 4.0, in allusion to the paradigm of the fourth industrial revolution. The application of Deep Learning in computer vision systems has been highlighted in Construction 4.0. Thus, the main contribution of this work is to present a systematic review of Deep Learning for vision systems under Construction 4.0, considering the most cited and most recent journal articles between 2017 and 2021 from Scopus database. For this, a research method selected and analyzed 76 published papers. Six main points were evaluated in the proposed methodology: study area, computer vision applications, Deep Learning methods, hyperparameter tuning, data augmentation, and future work. The following topics stand out as relevant perspectives and directions for continued advancement in this field of research: improving Deep Learning models, increasing the quality of databases, investigating the generality techniques and optimizing processing capacity.

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Data availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. http://www.scopus.com.

  2. https://www.acidb.ca/dataset.

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Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Finance Code 001 and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) - Chamada CNPq/MCTI/FNDCT \(\hbox {N}^\circ \) 18/2021 - UNIVERSAL.

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Authors and Affiliations

  1. Technologic and Exact Center, Federal University of Recôncavo da Bahia, Cruz das Almas, Brazil

    André L. C. Ottoni

  2. Electrical Engineering Graduate Program, Federal University of Bahia, Salvador, Brazil

    André L. C. Ottoni

  3. Department of Electrical and Computer Engineering, Federal University of Bahia, Salvador, Brazil

    Marcela S. Novo

  4. Department of Structural and Construction Engineering, Federal University of Bahia, Salvador, Brazil

    Dayana B. Costa

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  1. André L. C. Ottoni
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  3. Dayana B. Costa
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AO: conceptualization, methodology, writing, review and editing. MN: conceptualization, writing, review, editing and supervision. DC: conceptualization, review and supervision. All authors contributed to the final manuscript.

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Correspondence to André L. C. Ottoni.

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Ottoni, A.L.C., Novo, M.S. & Costa, D.B. Deep Learning for vision systems in Construction 4.0: a systematic review. SIViP 17, 1821–1829 (2023). https://doi.org/10.1007/s11760-022-02393-y

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