Publication detail

Enhanced Intelligent Identification of Concrete Cracks Using Multi-Layered Image Preprocessing-Aided Convolutional Neural Networks

FU, R. XU, H. WANG, Z. SHEN, L. CAO, M. LIU, T. NOVÁK, D.

Original Title

Enhanced Intelligent Identification of Concrete Cracks Using Multi-Layered Image Preprocessing-Aided Convolutional Neural Networks

Type

journal article in Web of Science

Language

English

Original Abstract

Crack identification plays an essential role in the health diagnosis of various concrete structures. Among different intelligent algorithms, the convolutional neural networks (CNNs) has been demonstrated as a promising tool capable of efficiently identifying the existence and evolution of concrete cracks by adaptively recognizing crack features from a large amount of concrete surface images. However, the accuracy as well as the versatility of conventional CNNs in crack identification is largely limited, due to the influence of noise contained in the background of the concrete surface images. The noise originates from highly diverse sources, such as light spots, blurs, surface roughness/wear/stains. With the aim of enhancing the accuracy, noise immunity, and versatility of CNN-based crack identification methods, a framework of enhanced intelligent identification of concrete cracks is established in this study, based on a hybrid utilization of conventional CNNs with a multi-layered image preprocessing strategy (MLP), of which the key components are homomorphic filtering and the Otsu thresholding method. Relying on the comparison and fine-tuning of classic CNN structures, networks for detection of crack position and identification of crack type are built, trained, and tested, based on a dataset composed of a large number of concrete crack images. The effectiveness and efficiency of the proposed framework involving the MLP and the CNN in crack identification are examined by comparative studies, with and without the implementation of the MLP strategy. Crack identification accuracy subject to different sources and levels of noise influence is investigated.

Keywords

concrete crack identification; convolutional neural network; homomorphic filtering; signal processing; structural health monitoring

Authors

FU, R.; XU, H.; WANG, Z.; SHEN, L.; CAO, M.; LIU, T.; NOVÁK, D.

Released

3. 4. 2020

Publisher

MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND

ISBN

1424-8220

Periodical

SENSORS

Year of study

20

Number

7

State

Swiss Confederation

Pages from

1

Pages to

24

Pages count

24

URL

https://www.mdpi.com/1424-8220/20/7/2021

BibTex

@article{BUT169098,
  author="Ronghua {Fu} and Hao {Xu} and Zijian {Wang} and Lei {Shen} and Maosen {Cao} and Tongwei {Liu} and Drahomír {Novák}",
  title="Enhanced Intelligent Identification of Concrete Cracks Using Multi-Layered Image Preprocessing-Aided Convolutional Neural Networks",
  journal="SENSORS",
  year="2020",
  volume="20",
  number="7",
  pages="1--24",
  doi="10.3390/s20072021",
  issn="1424-8220",
  url="https://www.mdpi.com/1424-8220/20/7/2021"
}