Qiqiang Chen1, *, Xinxin Gan2, Wei Huang1, Jingjing Feng1, H. Shim3
CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2201-2215, 2020, DOI:10.32604/cmc.2020.011191
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… More >
Login
Register
