@Article{cmes.2020.09122,
AUTHOR = {Duo Ma, Hongyuan Fang, Binghan Xue, Fuming Wang, Mohammed A. Msekh, Chiu Ling Chan},
TITLE = {Intelligent Detection Model Based on a Fully Convolutional  Neural Network for Pavement Cracks},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {123},
YEAR = {2020},
NUMBER = {3},
PAGES = {1267--1291},
URL = {http://www.techscience.com/CMES/v123n3/39315},
ISSN = {1526-1506},
ABSTRACT = {The crack is a common pavement failure problem. A lack of periodic 
maintenance will result in extending the cracks and damage the pavement, which will 
affect the normal use of the road. Therefore, it is significant to establish an efficient 
intelligent identification model for pavement cracks. The neural network is a method of 
simulating animal nervous systems using gradient descent to predict results by learning a 
weight matrix. It has been widely used in geotechnical engineering, computer vision, 
medicine, and other fields. However, there are three major problems in the application of 
neural networks to crack identification. There are too few layers, extracted crack features 
are not complete, and the method lacks the efficiency to calculate the whole picture. In 
this study, a fully convolutional neural network based on ResNet-101 is used to establish 
an intelligent identification model of pavement crack regions. This method, using a 
convolutional layer instead of a fully connected layer, realizes full convolution and 
accelerates calculation. The region proposals come from the feature map at the end of the 
base network, which avoids multiple computations of the same picture. Online hard 
example mining and data-augmentation techniques are adopted to improve the model’s 
recognition accuracy. We trained and tested Concrete Crack Images for Classification 
(CCIC), which is a public dataset collected using smartphones, and the Crack Image 
Database (CIDB), which was automatically collected using vehicle-mounted 
charge-coupled device cameras, with identification accuracy reaching 91.4% and 86.4%, 
respectively. The proposed model has a higher recognition accuracy and recall rate than 
Faster RCNN and different depth models, and can extract more complete and accurate 
crack features in CIDB. We also analyzed translation processing, fuzzy, scaling, and 
distorted images. The proposed model shows a strong robustness and stability, and can 
automatically identify image cracks of different forms. It has broad application prospects in practical engineering problems.},
DOI = {10.32604/cmes.2020.09122}
}