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Automated pavement crack detection is a challenging task that has beenresearched for decades due to the complicated pavement conditions in realworld. In this paper, a supervised method based on deep learning is proposed,which has the capability of dealing with different pavement conditions.Specifically, a convolutional neural network (CNN) is used to learn thestructure of the cracks from raw images, without any preprocessing. Smallpatches are extracted from crack images as inputs to generate a large trainingdatabase, a CNN is trained and crack detection is modeled as a multi-labelclassification problem. Typically, crack pixels are much fewer than non-crackpixels. To deal with the problem with severely imbalanced data, a strategy withmodifying the ratio of positive to negative samples is proposed. The method istested on two public databases and compared with five existing methods.Experimental results show that it outperforms the other methods.

Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network