  Anomaly detection and localization is an important vision problem, having
multiple applications. Effective and generic semantic segmentation of anomalous
regions on various different surfaces, where most anomalous regions inherently
do not have any obvious pattern, is still under active research. Periodic
health monitoring and fault (anomaly) detection in vast infrastructures, which
is an important safety-related task, is one such application area of
vision-based anomaly segmentation. However, the task is quite challenging due
to large variations in surface faults, texture-less construction
material/background, lighting conditions etc. Cracks are critical and frequent
surface faults that manifest as extreme zigzag-shaped thin, elongated regions.
They are among the hardest faults to detect, even with deep learning. In this
work, we address an open aspect of automatic crack segmentation problem, that
of generalizing and improving the performance of segmentation across a variety
of scenarios, by modeling the problem differently. We carefully study and
abstract the sub-problems involved and solve them in a broader context, making
our solution generic. On a variety of datasets related to surveillance of
different infrastructures, under varying conditions, our model consistently
outperforms the state-of-the-art algorithms by a significant margin, without
any bells-and-whistles. This performance advantage easily carried over in two
deployments of our model, tested against industry-provided datasets. Even
further, we could establish our model's performance for two manufacturing
quality inspection scenarios as well, where the defect types are not just crack
equivalents, but much more and different. Hence we hope that our model is
indeed a truly generic defect segmentation model.
