@Article{cmc.2020.011191,
AUTHOR = {Qiqiang Chen, Xinxin Gan, Wei Huang, Jingjing Feng, H. Shim},
TITLE = {Road Damage Detection and Classification Using Mask R-CNN  with DenseNet Backbone},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {3},
PAGES = {2201--2215},
URL = {http://www.techscience.com/cmc/v65n3/40164},
ISSN = {1546-2226},
ABSTRACT = {Automatic road damage detection using image processing is an important aspect
of road maintenance. It is also a challenging problem due to the inhomogeneity of road 
damage and complicated background in the road images. In recent years, deep 
convolutional neural network based methods have been used to address the challenges of
road damage detection and classification. In this paper, we propose a new approach to
address those challenges. This approach uses densely connected convolution networks as 
the backbone of the Mask R-CNN to effectively extract image feature, a feature pyramid 
network for combining multiple scales features, a region proposal network to generate the 
road damage region, and a fully convolutional neural network to classify the road damage
region and refine the region bounding box. This method can not only detect and classify the 
road damage, but also create a mask of the road damage. Experimental results show that the
proposed approach can achieve better results compared with other existing methods.},
DOI = {10.32604/cmc.2020.011191}
}