  Computer vision based methods have been explored in the past for detection of
railway track defects, but full automation has always been a challenge because
both traditional image processing methods and deep learning classifiers trained
from scratch fail to generalize that well to infinite novel scenarios seen in
the real world, given limited amount of labeled data. Advancements have been
made recently to make machine learning models utilize knowledge from a
different but related domain. In this paper, we show that even though similar
domain data is not available, transfer learning provides the model
understanding of other real world objects and enables training production scale
deep learning classifiers for uncontrolled real world data. Our models
efficiently detect both track defects like sunkinks, loose ballast and railway
assets like switches and signals. Models were validated with hours of track
videos recorded in different continents resulting in different weather
conditions, different ambience and surroundings. A track health index concept
has also been proposed to monitor complete rail network.
