Automatic detection of defective rail anchors

Rail line anchors/fasteners are the metallic components that attach each line with the sleepers. These are essential rail components as absence of these often result in derailments. Therefore in order to prevent dangerous situations and ensuring safety rail lines are periodically inspected. Rail inspection in many countries especially in third world countries, like Bangladesh, is performed manually by a trained human operator who periodically walks along the track searching for visual anomalies. This manual inspection is lengthy, laborious and subjective. This paper presents a machine vision-based technique to automatically detect the presence of rail line anchors/fasteners using Shi - Tomasi and Harris - Stephen feature detection algorithms. This approach has confirmed to successfully detect scenarios with both grounded and missing anchors invoked in the experiment, with an accuracy of 83.55%, thus proving its robustness.