CrackCLF: Automatic Pavement Crack Detection based on Closed-Loop
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Chong Li Zhun Fan Ying Chen Huibiao Lin Laura Moretti Giuseppe Loprencipe Weihua Sheng Kelvin C. P. Wang
Automatic pavement crack detection is an important task to ensure the
functional performances of pavements during their service life. Inspired by
deep learning (DL), the encoder-decoder framework is a powerful tool for crack
detection. However, these models are usually open-loop (OL) systems that tend
to treat thin cracks as the background. Meanwhile, these models can not
automatically correct errors in the prediction, nor can it adapt to the changes
of the environment to automatically extract and detect thin cracks. To tackle
this problem, we embed closed-loop feedback (CLF) into the neural network so
that the model could learn to correct errors on its own, based on generative
adversarial networks (GAN). The resulting model is called CrackCLF and includes
the front and back ends, i.e. segmentation and adversarial network. The front
end with U-shape framework is employed to generate crack maps, and the back end
with a multi-scale loss function is used to correct higher-order
inconsistencies between labels and crack maps (generated by the front end) to
address open-loop system issues. Empirical results show that the proposed
CrackCLF outperforms others methods on three public datasets. Moreover, the
proposed CLF can be defined as a plug and play module, which can be embedded
into different neural network models to improve their performances.