  Automated pavement crack detection is a challenging task that has been
researched for decades due to the complicated pavement conditions in real
world. 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 the
structure of the cracks from raw images, without any preprocessing. Small
patches are extracted from crack images as inputs to generate a large training
database, a CNN is trained and crack detection is modeled as a multi-label
classification problem. Typically, crack pixels are much fewer than non-crack
pixels. To deal with the problem with severely imbalanced data, a strategy with
modifying the ratio of positive to negative samples is proposed. The method is
tested on two public databases and compared with five existing methods.
Experimental results show that it outperforms the other methods.
