  Inspired by the development of deep learning in computer vision and object
detection, the proposed algorithm considers an encoder-decoder architecture
with hierarchical feature learning and dilated convolution, named
U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an
end-to-end method. Crack characteristics with multiple context information are
automatically able to learn and perform end-to-end crack detection. Then, a
multi-dilation module embedded in an encoder-decoder architecture is proposed.
The crack features of multiple context sizes can be integrated into the
multi-dilation module by dilation convolution with different dilatation rates,
which can obtain much more cracks information. Finally, the hierarchical
feature learning module is designed to obtain a multi-scale features from the
high to low-level convolutional layers, which are integrated to predict
pixel-wise crack detection. Some experiments on public crack databases using
118 images were performed and the results were compared with those obtained
with other methods on the same images. The results show that the proposed U-HDN
method achieves high performance because it can extract and fuse different
context sizes and different levels of feature maps than other algorithms.
