Memory-augment convolutional Autoencoder for unsupervised pavement crack classification

In urban construction, transportation system is an important part. However, the pavement cracks will occur because of using in long time and some external force collision, which has impacts on the safety and reliability of the whole transportation system. Therefore, it is necessary to detect the pavement cracks. The formation of pavement cracks is slow, and it is not concentrated on one place, so it is difficult to obtain enough images containing cracks to train. We propose a new pre-training method based on self-supervision study, which can obtain features closer to the target task from normal pavement images. In addition, we combine a memory-augment convolutional autoencoder and a hard-threshold module to improve the accuracy of the crack classification. Experiments conducted on the Mendeley Concrete crack dataset and Deep-Crack dataset demonstrate the good performance in pavement crack classification task.