  Crack detection has become an indispensable, interesting yet challenging task
in the computer vision community. Specially, pavement cracks have a highly
complex spatial structure, a low contrasting background and a weak spatial
continuity, posing a significant challenge to an effective crack detection
method. In this paper, we address these problems from a view that utilizes
contexts of the cracks and propose an end-to-end deep learning method to model
the context information flow. To precisely localize crack from an image, it is
critical to effectively extract and aggregate multi-granularity context,
including the fine-grained local context around the cracks (in spatial-level)
and the coarse-grained semantics (in segment-level). Concretely, in
Convolutional Neural Network (CNN), low-level features extracted by the shallow
layers represent the local information, while the deep layers extract the
semantic features. Additionally, a second main insight in this work is that the
semantic context should be an guidance to local context feature. By the above
insights, the proposed method we first apply the dilated convolution as the
backbone feature extractor to model local context, then we build a context
guidance module to leverage semantic context to guide local feature extraction
at multiple stages. To handle label alignment between stages, we apply the
Multiple Instance Learning (MIL) strategy to align the high-level feature to
the low-level ones in the stage-wise context flow. In addition, compared with
these public crack datasets, to our best knowledge, we release the largest,
most complex and most challenging Bitumen Pavement Crack (BPC) dataset. The
experimental results on the three crack datasets demonstrate that the proposed
method performs well and outperforms the current state-of-the-art methods.
