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Extended efficient convolutional neural network for concrete crack detection with illustrated merits

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F23%3APU150258" target="_blank" >RIV/00216305:26110/23:PU150258 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.sciencedirect.com/science/article/pii/S0926580523003588?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0926580523003588?via%3Dihub</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.autcon.2023.105098" target="_blank" >10.1016/j.autcon.2023.105098</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Extended efficient convolutional neural network for concrete crack detection with illustrated merits

  • Original language description

    An efficient convolutional neural network (CNN), called EfficientNetV2, was recently developed. The early blocks of EfficientNetV2 have structural characteristics that lead to higher training speeds than state-of-the-art CNNs. Inspired by EfficientNetV2, extended research was conducted in this study to determine whether the early, middle, and late blocks of CNNs should have respective structural characteristics to achieve higher efficiency. Based on comprehensive studies, three tactics were proposed, which underpinned a swift CNN called StairNet. StairNet was subsequently equipped into faster region-based CNN framework, producing Faster R-Stair. The presented StairNet and Faster R-Stair were validated on two datasets, respectively: Dataset1 comprising a pair of open-source datasets and a dataset of images captured in real-world conditions; Dataset2 derived from Dataset1, consisting of more complicated object modes, with the purpose of mimicking the coexistence of multiple cracks under real conditions. Experimental results showed that StairNet outperforms EfficientNetV2, GoogLeNet, VGG16_BN, ResNet34, and MobileNetV3 in efficiency of crack classification and detection. A Faster R-Stair concrete crack-detection software platform was also developed. The software platform and an unmanned aerial vehicle were used to detect concrete road cracks at a university in Nanjing, China. The developed system has a swift detection process, with high speed and excellent results.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20101 - Civil engineering

Result continuities

  • Project

    <a href="/en/project/TM04000012" target="_blank" >TM04000012: A concrete bridge health interpretation system based on mutual boost of big data and physical mechanism</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    AUTOMATION IN CONSTRUCTION

  • ISSN

    0926-5805

  • e-ISSN

    1872-7891

  • Volume of the periodical

    156

  • Issue of the periodical within the volume

    105098

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    23

  • Pages from-to

    „“-„“

  • UT code for WoS article

    001085394600001

  • EID of the result in the Scopus database

    2-s2.0-85172706718