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Deep Learning-Based Concrete Crack Detection Using YOLO Architecture

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Artificial Intelligence and Smart Vehicles (ICAISV 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1883))

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Abstract

Buildings, bridges and dams are important infrastructures which containing concrete; hence it is essential to understand how the concrete cracks when it is in service condition. The most common flaw in concrete structures is cracking, which reduces load-carrying capacity, stiffness, and durability. In this research, we employ deep learning methods to detect surface cracks in concrete buildings. The purpose of this research was to compare the detection capabilities of the YOLOv8 and YOLOv5 models. The models were quantitatively evaluated using evaluation measures like accuracy, recall, and mean average precision to analyze their detection performance. This study demonstrates that the YOLOv8 algorithm exhibits superior performance in detection accuracy compared to the YOLOv5 algorithms. Results show that the YOLOv81 model has the highest precision value, the YOLOv8x has the highest recall value, and the YOLOv8m and YOLOv8x have the highest mAP@50 value. Also, the mAP@50–90 values of these models are approximately equal and are the highest among other models.

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Acknowledgements

Without the central library of Sardar Vallabhbhai National Institute of Technology (SVNIT), we would have had a much tougher job referencing reliable sources for this research. Thus, the authors are grateful to the SVNIT library in Surat, India, for making the online database available to authors. The authors would also like to express their appreciation to the anonymous reviewers whose comments and suggestions helped to improve the quality of the article.

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Authors and Affiliations

  1. Department of Civil Engineering, Hakim Sabzevari University, Sabzevar, Iran

    Elham Nabizadeh

  2. Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India

    Anant Parghi

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  1. Elham Nabizadeh
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  2. Anant Parghi
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Correspondence to Elham Nabizadeh .

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Editors and Affiliations

  1. Amirkabir University of Technology, Tehran, Iran

    Mehdi Ghatee

  2. Amirkabir University of Technology, Tehran, Iran

    S. Mehdi Hashemi

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Nabizadeh, E., Parghi, A. (2023). Deep Learning-Based Concrete Crack Detection Using YOLO Architecture. In: Ghatee, M., Hashemi, S.M. (eds) Artificial Intelligence and Smart Vehicles. ICAISV 2023. Communications in Computer and Information Science, vol 1883. Springer, Cham. https://doi.org/10.1007/978-3-031-43763-2_11

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  • DOI: https://doi.org/10.1007/978-3-031-43763-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43762-5

  • Online ISBN: 978-3-031-43763-2

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