Abstract: A single tunnel image crack extraction algorithm based on fully convolutional neural network is proposed, which can effectively avoid the interference of pseudo-crack noise points in complex backgrounds and achieve accurate segmentation of tunnel cracks. First, a deep residual network model is constructed to extract crack features. Second, in order to recover the size of the crack feature map as well as the crack details, the feature map size is recovered using a deconvolution operation in an improved fully convolutional neural network. In order to improve the fineness of crack extraction, a detail repair module is proposed, which can maintain the integrity and edge details of cracks. Finally, a crack dataset NUAACrack-2000 is published, which contains 2000 tunnel crack images and accurately labeled labels. Extensive experiments show that the proposed algorithm outperforms traditional image segmentation algorithms in avoiding noise point interference. It is superior to the mainstream crack extraction algorithms based on machine learning in preserving the integrity of the extracted cracks and processing edge details.

Key words: crack extraction, image segmentation, NUAACrack-2000, fully convolutional neural network