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.

为了确保道路安全，需要进行路面裂缝检测。本研究提出一种基于深度学习的新型网络体系结构，称为特征金字塔和分层增强网络（FPHBN），以解决路面裂缝检测中的难题。实验表明，该方法在准确性和通用性方面优于现有的检测、边缘检测和语义分割方法。