Intelligent Detection Model Based on a Fully Convolutional Neural Network for Pavement Cracks

The crack is a common pavement failure problem.A lack of periodic maintenance will result in extending the cracks and damage the pavement,which will affect the normal use of the road.Therefore,it is significant to establish an efficient intelligent identification model for pavement cracks.The neural network is a method of simulating animal nervous systems using gradient descent to predict results by learning a weight matrix.It has been widely used in geotechnical engineering,computer vision,medicine,and other fields.However,there are three major problems in the application of neural networks to crack identification.There are too few layers,extracted crack features are not complete,and the method lacks the efficiency to calculate the whole picture.In this study,a fully convolutional neural network based on ResNet-101 is used to establish an intelligent identification model of pavement crack regions.This method,using a convolutional layer instead of a fully connected layer,realizes full convolution and accelerates calculation.The region proposals come from the feature map at the end of the base network,which avoids multiple computations of the same picture.Online hard example mining and data-augmentation techniques are adopted to improve the model’s recognition accuracy.We trained and tested Concrete Crack Images for Classification(CCIC),which is a public dataset collected using smartphones,and the Crack Image Database(CIDB),which was automatically collected using vehicle-mounted charge-coupled device cameras,with identification accuracy reaching 91.4%and 86.4%,respectively.The proposed model has a higher recognition accuracy and recall rate than Faster RCNN and different depth models,and can extract more complete and accurate crack features in CIDB.We also analyzed translation processing,fuzzy,scaling,and distorted images.The proposed model shows a strong robustness and stability,and can automatically identify image cracks of different forms.It has broad application prospects in practical engineering problems.

Digital image correlation based high-speed crack tip locatingmethod and its application

Based on a digital image correlation（DIC）method with the measurements of a high speed crack＇s displacement and strain fields,a technique to accurately and automatically locate its crack tip has been developed.The crack tip is identified by finding the abrupt jump on the opening（or dislocation）curve of a point on the trace of the crack propagation,while the opening is measured through a virtual extensometer technique and the abrupt jump is identified by finding the peak on the curve.The method was verified using a specially designed experiment and applied to measure the critical crack tip opening angle of a rock sample.Because the involvement of analytical models has been avoided and then the good performance could be ensured for low resolution speckle images,this technique is expected to be very useful in the quantitative study of high speed cracks in experiments using high speed cameras.

A review of deep learning methods for pixel-level crack detection

Cracks are a major sign of aging transportation infrastructure.The detection and repair of cracks is the key to ensuring the overall safety of the transportation infrastructure.In recent years,due to the remarkable success of deep learning(DL)in the field of crack detection,many researches have been devoted to developing pixel-level crack image segmentation(CIS)models based on DL to improve crack detection accuracy,but as far as we know there is no review of DL-based CIS methods yet.To address this gap,we present a comprehensive thematic survey of DL-based CIS techniques.Our review offers several contributions to the CIS area.First,more than 40 papers of journal or top conference most published in the last three years are identified and collected based on the systematic literature review method.Second,according to the backbone network architecture of the models proposed in them,they are grouped into 10 topics:FCN,U-Net,encoder-decoder model,multi-scale,attention mechanism,transformer,two-stage detection,multi-modal fusion,unsupervised learning and weakly supervised learning,to be reviewed.Meanwhile,our survey focuses on discussing strengths and limitations of the models in each topic so as to reveal the latest research progress in the CIS field.Third,publicly accessible data sets,evaluation metrics,and loss functions that can be used for pixel-level crack detection are systematically introduced and summarized to facilitate researchers to select suitable components according to their own research tasks.Finally,we discuss six common problems and existing solutions to them in the field of DL-based CIS,and then suggest eight possible future research directions in this field.