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
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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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