Autonomous Inspection System for Railroads

From January 2010 to November 2019, derailments in the United States have cost railroad companies over $350,000,000 in damages in addition to the costly environmental remediation cleanups and injuries. The State of Virginia is home to many small scale railroads that have less than a total of 100 miles of track between destinations. Manual inspection is common, but inspectors can miss small details, leading many railroads to supplement with autonomous inspection. Many small scale railroads do not have the resources to autonomously inspect their tracks and cannot afford the cost of large scale railroad inspection equipment in addition to the resources needed to run these inspection systems. Surface level defects are the most common reason for derailment and can lead to serious damage to the train and track if left untreated. The ultimate goal of the project is to prevent or greatly decrease the likelihood of train derailment by focusing on the detection of surface level defects on rails, which will have an impact on all railroads while especially helping local short line railroads.By creating an inspection system that works for all railroad companies, anyone can more accurately and precisely find surface level defects than current manual inspections. Working with industry experts, the most common and dangerous surface level defects and the appropriate methods of detection were determined. The resulting solution is the design of a small autonomous rail cart that takes images of the track from a video feed and sorts them into "good" or "bad" photos using machine learning. Locations with "bad" rail can then be re-inspected by manual inspectors to determine the severity of the problem and work towards fixing it. This system will identify and alert users to dangerous levels of rail damage while providing a cheaper inspection alternative for smaller railroads. It also acknowledges and takes advantage of the expertise and abilities of trained manual inspectors to make the final decision.