Detail publikace

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

FU, R. CAO, M. NOVÁK, D. QIAN, X. ALKAYEM, N.

Originální název

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

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

EfficientNet; Three tactics; CNN performance improvement; StairNet; Concrete crack detection; Unmanned aerial vehicle; Software

Autoři

FU, R.; CAO, M.; NOVÁK, D.; QIAN, X.; ALKAYEM, N.

Vydáno

27. 9. 2023

Nakladatel

ELSEVIER

Místo

AMSTERDAM

ISSN

0926-5805

Periodikum

AUTOMATION IN CONSTRUCTION

Ročník

156

Číslo

105098

Stát

Nizozemsko

Strany počet

23

URL

https://www.sciencedirect.com/science/article/pii/S0926580523003588?via%3Dihub

BibTex

@article{BUT187229,
  author="Ronghua {Fu} and Maosen {Cao} and Drahomír {Novák} and Xiangdong {Qian} and Nizar Faisal {Alkayem}",
  title="Extended efficient convolutional neural network for concrete crack detection with illustrated merits",
  journal="AUTOMATION IN CONSTRUCTION",
  year="2023",
  volume="156",
  number="105098",
  pages="23",
  doi="10.1016/j.autcon.2023.105098",
  issn="0926-5805",
  url="https://www.sciencedirect.com/science/article/pii/S0926580523003588?via%3Dihub"
}