  Pavement crack detection is a critical task for insuring road safety. Manual
crack detection is extremely time-consuming. Therefore, an automatic road crack
detection method is required to boost this progress. However, it remains a
challenging task due to the intensity inhomogeneity of cracks and complexity of
the background, e.g., the low contrast with surrounding pavements and possible
shadows with similar intensity. Inspired by recent advances of deep learning in
computer vision, we propose a novel network architecture, named Feature Pyramid
and Hierarchical Boosting Network (FPHBN), for pavement crack detection. The
proposed network integrates semantic information to low-level features for
crack detection in a feature pyramid way. And, it balances the contribution of
both easy and hard samples to loss by nested sample reweighting in a
hierarchical way. To demonstrate the superiority and generality of the proposed
method, we evaluate the proposed method on five crack datasets and compare it
with state-of-the-art crack detection, edge detection, semantic segmentation
methods. Extensive experiments show that the proposed method outperforms these
state-of-the-art methods in terms of accuracy and generality.
