  Millions of human casualties resulting from train accidents globally are
caused by the inefficient, manual track inspections. Government agencies are
seriously concerned about the safe operations of the rail industry after series
of accidents reported across e USA and around the globe, mainly attributed to
track defects. Casualties resulting from track defects result in billions of
dollars loss in public and private investments and loss of revenue due to
downtime, ultimately resulting in loss of the public's confidence. The manual,
mundane, and expensive monitoring of rail track safety can be transform through
the use of drones, computer vision, and machine learning. The primary goal of
this study is to develop multiple algorithms that implement supervised and
semi-supervised learning that accurately analyze whether a track is safe or
unsafe based on simulated training data of train tracks. This includes being
able to develop a Convolutional Neural Network that can identify track defects
using supervised learning without having to specify a particular algorithm for
detecting those defects, and that the new model would both speed up and improve
the quality of the track defect detection process, accompanied with a computer
vision image-processing algorithm. Our other goals included designing and
building a prototype representation of train tracks to simulate track defects,
to precisely and consistently conduct the visual inspection using drones.
Ultimately, the goal demonstrates that the state of good repairs in railway
tracks can be attained through the use of drones, computer vision and machine
learning.
