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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

<a href="/cs/project/TM04000012" target="_blank" >TM04000012: Systém pro zjišťování stavu betonových mostů založený na na vzájemné podpoře velkých dat a mechaniky</a><br>

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

AUTOMATION IN CONSTRUCTION

ISSN

0926-5805

e-ISSN

1872-7891

Svazek periodika

156

Číslo periodika v rámci svazku

105098

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

23

Strana od-do

„“-„“

Kód UT WoS článku

001085394600001

EID výsledku v databázi Scopus

2-s2.0-85172706718