Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces
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| Định dạng: | Bài Báo |
| Ngôn ngữ: | English |
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Applied Computational Intelligence and Soft Computing
2021
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| Truy cập trực tuyến: | https://www.hindawi.com/journals/acisc/2021/8858545/ https://dlib.phenikaa-uni.edu.vn/handle/PNK/1853 |
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oai:localhost:PNK-18532022-08-17T05:54:43Z Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces Le, Tien-Thinh Nguyen, Van-Hai Le, Minh Vuong Q2 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. 2021-06-18T03:09:25Z 2021-06-18T03:09:25Z 2021 Article Working Paper https://www.hindawi.com/journals/acisc/2021/8858545/ https://dlib.phenikaa-uni.edu.vn/handle/PNK/1853 doi.org/10.1155/2021/8858545 en application/pdf Applied Computational Intelligence and Soft Computing |
| institution |
Digital Phenikaa |
| collection |
Digital Phenikaa |
| language |
English |
| description |
Q2 |
| author2 |
Nguyen, Van-Hai |
| author_facet |
Nguyen, Van-Hai Le, Tien-Thinh |
| format |
Article |
| author |
Le, Tien-Thinh |
| spellingShingle |
Le, Tien-Thinh Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces |
| author_sort |
Le, Tien-Thinh |
| title |
Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces |
| title_short |
Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces |
| title_full |
Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces |
| title_fullStr |
Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces |
| title_full_unstemmed |
Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces |
| title_sort |
development of deep learning model for the recognition of cracks on concrete surfaces |
| publisher |
Applied Computational Intelligence and Soft Computing |
| publishDate |
2021 |
| url |
https://www.hindawi.com/journals/acisc/2021/8858545/ https://dlib.phenikaa-uni.edu.vn/handle/PNK/1853 |
| _version_ |
1751856253905141760 |
| score |
8.891145 |