Road Damage Detection and Classification Using Mask R-CNN with DenseNet Backbone

Automatic road damage detection using image processing is an important aspectof 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 ofroad damage detection and classification. In this paper, we propose a new approach toaddress 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 damageregion 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 theproposed approach can achieve better results compared with other existing methods.