Demonstration of Image-Based Change Detection using a Prototype Drone-Based Track Safety Inspection 3/16/2018 Todays prevailing methods of visual track inspection tend to be expensive, disruptive to operations, and have potential to be less thorough than preferred. Machine vision technology emerged in the rail sector over a decade ago to help alleviate these concerns; however, due to the uncontrolled nature of rail environments, the technology has not achieved its expected potential. Image-based change detection emerged over 25 years ago and is well-suited for application to aerial imagery. Preliminary results indicate that the simplest form of change detection (image differencing) is able to highlight many relevant track conditions larger than a configurable size threshold with a detection probability near 100 percent. The use of change detection transforms the rail sector machine vision problem into a relevant/non-relevant determination a problem expected to be handled robustly with the aid of state-of-the-art machine learning. The research proposed here intends to demonstrate a prototype Drone-Based Track Safety Inspection System using change detection to identify track conditions that are then automatically classified as relevant or non-relevant with assistance from machine learning. If successful, the approach is expected to reduce the cost of visual track inspection with simultaneous improvements in effectiveness, equating to increased operational safety for trains.
