  Timely, accurate and automatic detection of pavement cracks is necessary for
making cost-effective decisions concerning road maintenance. Conventional crack
detection algorithms focus on the design of single or multiple crack features
and classifiers. However, complicated topological structures, varying degrees
of damage and oil stains make the design of crack features difficult. In
addition, the contextual information around a crack is not investigated
extensively in the design process. Accordingly, these design features have
limited discriminative adaptability and cannot fuse effectively with the
classifiers. To solve these problems, this paper proposes a deep learning
network for pavement crack detection. Using the Encoder-Decoder structure,
crack characteristics with multiple contexts are automatically learned, and
end-to-end crack detection is achieved. Specifically, we first propose the
Multi-Dilation (MD) module, which can synthesize the crack features of multiple
context sizes via dilated convolution with multiple rates. The crack MD
features obtained in this module can describe cracks of different widths and
topologies. Next, we propose the SE-Upsampling (SEU) module, which uses the
Squeeze-and-Excitation learning operation to optimize the MD features. Finally,
the above two modules are integrated to develop the fast crack detection
network, namely, FPCNet. This network continuously optimizes the MD features
step-by-step to realize fast pixel-level crack detection. Experiments are
conducted on challenging public CFD datasets and G45 crack datasets involving
various crack types under different shooting conditions. The distinct
performance and speed improvements over all the datasets demonstrate that the
proposed method outperforms other state-of-the-art crack detection methods.
