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- Title
Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures.
- Authors
Ali, Luqman; Alnajjar, Fady; Jassmi, Hamad Al; Gocho, Munkhjargal; Khan, Wasif; Serhani, M. Adel; Yoo, Doo-Yeol
- Abstract
This paper proposes a customized convolutional neural network for crack detection in concrete structures. The proposed method is compared to four existing deep learning methods based on training data size, data heterogeneity, network complexity, and the number of epochs. The performance of the proposed convolutional neural network (CNN) model is evaluated and compared to pretrained networks, i.e., the VGG-16, VGG-19, ResNet-50, and Inception V3 models, on eight datasets of different sizes, created from two public datasets. For each model, the evaluation considered computational time, crack localization results, and classification measures, e.g., accuracy, precision, recall, and F1-score. Experimental results demonstrated that training data size and heterogeneity among data samples significantly affect model performance. All models demonstrated promising performance on a limited number of diverse training data; however, increasing the training data size and reducing diversity reduced generalization performance, and led to overfitting. The proposed customized CNN and VGG-16 models outperformed the other methods in terms of classification, localization, and computational time on a small amount of data, and the results indicate that these two models demonstrate superior crack detection and localization for concrete structures.
- Subjects
DEEP learning; CONVOLUTIONAL neural networks; CRACKING of concrete; CONCRETE
- Publication
Sensors (14248220), 2021, Vol 21, Issue 5, p1688
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s21051688