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

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.

Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

In recent years,great attention has focused on the development of automated procedures for infrastructures control.Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural conditions.The paper proposes a multi-level strategy,designed and implemented on the basis of periodic structural monitoring oriented to a cost-and time-efficient tunnel control plan.Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations.In a supervised learning framework,Ground Penetrating Radar(GPR)profiles and the revealed structural phenomena have been used as input and output to train and test such networks.Image-based analysis and integrative investigations involving video-endoscopy,core drilling,jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database.The degree of detail and accuracy achieved in identifying a structural condition is high.As a result,this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing,and thus also to reduce the time and costs associated with inspections made by highly specialized technicians.