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

Abstract

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.

A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning

“…Moreover, to demonstrate the capability of the newly constructed CV-SSA-SVC, Random Forest Classification (RFC) model [97], Backpropagation Artificial Neural Network (BPANN) [98,99], and Convolutional Neural Network (CNN) models [100] have been selected as benchmark approaches. e RFC, BPANN, and CNN are capable classifiers and have been widely employed in pattern recognition and particularly in data-driven or structural health monitoring based on computer vision [101][102][103][104][105][106][107][108][109][110][111][112].…”

Concrete Spalling Severity Classification Using Image Texture Analysis and a Novel Jellyfish Search Optimized Machine Learning Approach

“…In addition, to demonstrate the JSO-SVC predictive performance, the random forest classification (RFC) model [79] and convolutional neural network (CNN) models [80] have been employed as benchmark approaches. e RFC and CNN are selected for result comparison in this study because these two machine learning approaches have been successfully applied in various works related to computer vision-based or nondestructive testing-based structural health monitoring/diagnosis [14,26,[81][82][83][84][85][86][87][88].…”

“…In addition, although machine learning methods have been extensively used in computer vision-based structural health monitoring [3,12,[24][25][26], hybrid approaches that combine the strengths of machine learning and metaheuristic algorithms are rarely investigated in this field especially for concrete spall recognition. Metaheuristic algorithms can be used to optimize the learning phase of machine learning models and therefore help to achieve better predictive performances [27][28][29][30][31][32][33].…”

Automatic Pixel-Level Pavement Crack Recognition Using a Deep Feature Aggregation Segmentation Network with a scSE Attention Mechanism Module

“…This model enabled the employment of deep learning algorithms using low-power computational devices for a hassle-free monitoring of civil structures. Le et al proposed a crack detection algorithm based on a DL convolutional neural network, which achieved a good crack classification effect [ 30 ]. Ali et al proposed a customized convolutional neural network for crack detection in concrete structures, which can achieve higher crack detection accuracy [ 31 ].…”