Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures.

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 signific

SUBMITTER: Ali L 

PROVIDER: S-EPMC7957757 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature