  Surface cracks are a common sight on public infrastructure nowadays. Recent
work has been addressing this problem by supporting structural maintenance
measures using machine learning methods. Those methods are used to segment
surface cracks from their background, making them easier to localize. However,
a common issue is that to create a well-functioning algorithm, the training
data needs to have detailed annotations of pixels that belong to cracks. Our
work proposes a weakly supervised approach that leverages a CNN classifier in a
novel way to create surface crack pseudo labels. First, we use the classifier
to create a rough crack localization map by using its class activation maps and
a patch based classification approach and fuse this with a thresholding based
approach to segment the mostly darker crack pixels. The classifier assists in
suppressing noise from the background regions, which commonly are incorrectly
highlighted as cracks by standard thresholding methods. Then, the pseudo labels
can be used in an end-to-end approach when training a standard CNN for surface
crack segmentation. Our method is shown to yield sufficiently accurate pseudo
labels. Those labels, incorporated into segmentation CNN training using
multiple recent crack segmentation architectures, achieve comparable
performance to fully supervised methods on four popular crack segmentation
datasets.
