Open Access
ARTICLE
Road Damage Detection and Classification Using Mask R-CNN with DenseNet Backbone
Qiqiang Chen1, *, Xinxin Gan2, Wei Huang1, Jingjing Feng1, H. Shim3
1 School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou,
450000, China.
2 SIPPR Engineering Group Co., Ltd., Zhengzhou, 450000, China.
3 College of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, 440746, Korea.
* Corresponding Author: Qiqiang Chen. Email: .
Computers, Materials & Continua 2020, 65(3), 2201-2215. https://doi.org/10.32604/cmc.2020.011191
Received 24 April 2020; Accepted 08 June 2020; Issue published 16 September 2020
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.
Keywords
Cite This Article
Q. Chen, X. Gan, W. Huang, J. Feng and H. Shim, "Road damage detection and classification using mask r-cnn with densenet backbone,"
Computers, Materials & Continua, vol. 65, no.3, pp. 2201–2215, 2020.
Citations