Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces

This paper is devoted to the development of a deep learning- (DL-) based model to detect crack fractures on concrete surfaces. The developed model for the classification of images was based on a DL Convolutional Neural Network (CNN). To train and validate the CNN model, a database containing 40,000...

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Vydané v:Applied Computational Intelligence and Soft Computing Ročník 2021; s. 1 - 10
Hlavní autori: Le, Tien-Thinh, Nguyen, Van-Hai, Le, Minh Vuong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Hindawi 2021
John Wiley & Sons, Inc
Wiley
Predmet:
Algorithms
Artificial neural networks
Asphalt pavements
Cable television broadcasting industry
Concrete
Cracks
Datasets
Deep learning
Drone aircraft
Error analysis
Finite element analysis
Fractures
Image classification
Infrastructure
Inspection
Machine learning
Model accuracy
Neural networks
Recall
Repair & maintenance
Surface finish
Unmanned aerial vehicles
United States
ISSN:1687-9724, 1687-9732
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Shrnutí:This paper is devoted to the development of a deep learning- (DL-) based model to detect crack fractures on concrete surfaces. The developed model for the classification of images was based on a DL Convolutional Neural Network (CNN). To train and validate the CNN model, a database containing 40,000 images of concrete surfaces (with and without cracks) was collected from the available literature. Several conditions on the concrete surfaces were taken into account such as illumination and surface finish (i.e., exposed, plastering, and paint). Various error measurement criteria such as accuracy, precision, recall, specificity, and F1-score were employed for accessing the quality of the developed model. Results showed that for the training dataset (50% of the database), the precision, recall, specificity, F1-score, and accuracy were 99.5%, 99.8%, 99.5%, 99.7%, and 99.7%, respectively. On the other hand, for the validating dataset, the precision, recall, specificity, F1-score, and accuracy are 96.5%, 98.8%, 96.6%, 97.7%, and 97.7%, respectively. Thus, the developed CNN model may be considered valid because it performs the classification of cracks well using the testing data. It is also confirmed that the developed DL-based model was robust and efficient, as it can take into account different conditions on the concrete surfaces. The CNN model developed in this study was compared with other works in the literature, showing that the CNN model could improve the accuracy of image classification, in comparison with previously published results. Finally, in further work, such model could be combined with Unmanned Aerial Vehicles (UAVs) to increase the productivity of concrete infrastructure inspection.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1687-9724
1687-9732
DOI:10.1155/2021/8858545