  Automatically detecting or segmenting cracks in images can help in reducing
the cost of maintenance or operations. Detecting, measuring and quantifying
cracks for distress analysis in challenging background scenarios is a difficult
task as there is no clear boundary that separates cracks from the background.
Developed algorithms should handle the inherent challenges associated with
data. Some of the perceptually noted challenges are color, intensity, depth,
blur, motion-blur, orientation, different region of interest (ROI) for the
defect, scale, illumination, complex and challenging background, etc. These
variations occur across (crack inter class) and within images (crack
intra-class variabilities). Overall, there is significant background (inter)
and foreground (intra-class) variability. In this work, we have attempted to
reduce the effect of these variations in challenging background scenarios. We
have proposed a stochastic width (SW) approach to reduce the effect of these
variations. Our proposed approach improves detectability and significantly
reduces false positives and negatives. We have measured the performance of our
algorithm objectively in terms of mean IoU, false positives and negatives and
subjectively in terms of perceptual quality.
