  Safety-critical infrastructures, such as bridges, are periodically inspected
to check for existing damage, such as fatigue cracks and corrosion, and to
guarantee the safe use of the infrastructure. Visual inspection is the most
frequent type of general inspection, despite the fact that its detection
capability is rather limited, especially for fatigue cracks. Machine learning
algorithms can be used for augmenting the capability of classical visual
inspection of bridge structures, however, the implementation of such an
algorithm requires a massive annotated training dataset, which is
time-consuming to produce. This paper proposes a semi-automatic crack
segmentation tool that eases the manual segmentation of cracks on images needed
to create a training dataset for a machine learning algorithm. Also, it can be
used to measure the geometry of the crack. This tool makes use of an image
processing algorithm, which was initially developed for the analysis of
vascular systems on retinal images. The algorithm relies on a multi-orientation
wavelet transform, which is applied to the image to construct the so-called
"orientation scores", i.e. a modified version of the image. Afterwards, the
filtered orientation scores are used to formulate an optimal path problem that
identifies the crack. The globally optimal path between manually selected crack
endpoints is computed, using a state-of-the-art geometric tracking method. The
pixel-wise segmentation is done afterwards using the obtained crack path. The
proposed method outperforms fully automatic methods and shows potential to be
an adequate alternative to the manual data annotation.
