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Crack Detection on Brick Walls by Convolutional Neural Networks Using the Methods of Sub-dataset Generation and Matching

  • Conference paper
  • First Online:
Deep Learning Theory and Applications (DeLTA 2020, DeLTA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1854))

  • 106 Accesses

Abstract

The appearance of cracks is considered an initial sign of the deterioration of structures such as concrete and brick walls. Crack detection plays an important role in ensuring the safety and durability of structures. Conventionally, a maintenance engineer performs crack detection manually, which is laborious and time-consuming. Therefore, a systematic crack detection method is required. Among the existing crack detection methods, convolutional neural networks (CNNs) are more effective; however, CNNs often fail in the case of brick walls. There are several types of bricks, and some may appear to have cracks owing to their structure. Additionally, the joining points of bricks may appear as cracks; therefore, CNN fails. It is theorized that CNN performance can be improved if sub-datasets are generated based on the image attributes, and a proper sub-dataset is selected by matching the test image with the sub-datasets. In this study, sub-dataset generation and matching methods are proposed to improve the performance of crack detection in brick walls using CNN. CNN training is conducted with each sub-dataset generated by the proposed sub-dataset generation method, while crack detection is performed using a proper trained CNN that is selected using the proposed matching method. For numerical experiments, training datasets are first prepared by manual image cropping and rotation, after which the performance of crack detection is evaluated by cross-validation. Numerical experiments show that the proposed method improves crack detection in brick walls. This study will help to ensure the safety of structures as well as the safety of human life.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Faculty of Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh

    Mehedi Hasan Talukder

  2. Department of Industrial Engineering and Management, Faculty of Engineering, Kanagawa University, Yokohama, Japan

    Shuhei Ota, Masato Takanokura & Nobuaki Ishii

Authors
  1. Mehedi Hasan Talukder
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  2. Shuhei Ota
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  3. Masato Takanokura
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  4. Nobuaki Ishii
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Corresponding author

Correspondence to Mehedi Hasan Talukder .

Editor information

Editors and Affiliations

  1. University of Lisbon, Lisbon, Portugal

    Ana Fred

  2. Università degli Studi di Napoli, Napoli, Italy

    Carlo Sansone

  3. Université Paris-Est Créteil (UPEC), Créteil, France

    Kurosh Madani

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Talukder, M.H., Ota, S., Takanokura, M., Ishii, N. (2023). Crack Detection on Brick Walls by Convolutional Neural Networks Using the Methods of Sub-dataset Generation and Matching. In: Fred, A., Sansone, C., Madani, K. (eds) Deep Learning Theory and Applications. DeLTA DeLTA 2020 2021. Communications in Computer and Information Science, vol 1854. Springer, Cham. https://doi.org/10.1007/978-3-031-37320-6_7

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  • DOI: https://doi.org/10.1007/978-3-031-37320-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37319-0

  • Online ISBN: 978-3-031-37320-6

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