Feasibility analysis of convolution neural network models for classification of concrete cracks in Smart City structures

Cracks are one of the forms of damage to concrete structures that debase the strength and durability of the building material and may pose a danger to the living being associated with it. Proper and regular diagnosis of concrete cracks is therefore necessary. Nowadays, for the more accurate identification and classification of cracks, various automated crack detection techniques are employed over a manual human inspection. Convolution Neural Network (CNN) has shown excellent performance in image processing. Thus, it is becoming the mainstream choice to replace the manual crack classification techniques, but this technique requires huge labeled data for training. Transfer learning is a strategy that tackles this issue by using pre-trained models. This work first time strives to classify concrete surface cracks by re-training of six pre-trained deep CNN models such as VGG-16, DenseNet-121, Inception-v3, ResNet-50, Xception, and InceptionResNet-v2 using transfer learning and comparing them with different metrics, such as Accuracy, Precision, Recall, F1-Score, Cohen Kappa, ROC AUC, and Error Rate in order to find the model with the best suitability. A dataset from two separate sources is considered for the re-training of pre-trained models, for the classification of cracks on concrete surfaces. Initially, the selective crack and non-crack images of the Mendeley dataset are considered, and later, a new dataset is used. As a result, the re-trained classifier of CNN models provides a consistent performance with an accuracy range of 0.95 to 0.99 on the first dataset and 0.85 to 0.98 on the new dataset. The results show that these CNN variants can produce the best outcome when finding cracks in the real situation and have strong generalization capabilities.