The United States has nearly 140,000 miles of railroad tracks that transport one-third of U.S. exports, delivering five million tons of freight, and 100,000 passengers daily. It is the responsibility of the railroad entity to ensure the good working condition (maintenance, replacement, and upgrade) of most of the nation’s track, bridges, and connections at ports.
Due to the tremendous magnitude of railroad infrastructure within the United States, the task of inspecting and maintaining tracks has not kept up with technology but there has been a very recent adoption of drone technology. The main difficulty in the boots-on-the-ground inspection model has been the lack of automation, scalability, and repeatability resulting in huge delays in reporting, sometimes taking months with little to no actionable information.
One of the major challenges facing railroad networks is preventing obvious failures in tracks. Preventing potential malfunctions requires inspecting thousands of miles of the track while avoiding risk to inspectors and traffic interference.
Regular maintenance and inspection procedures often require personnel to be within the rail corridor to perform visual inspections. A range of safety protocols have been introduced to minimize risks, but they involve expensive track possession to inspect track sections, which sometimes lack clear accessible safety zones. Unmanned Aerial System drones provide an easier alternative for railway inspections and minimize the requirement of additional human resources. Drones are increasingly becoming recognized as having an important role in assisting the rail industry, and their adoption is challenging traditional models and operating procedures for all involved.
Vegetation management remains one of the most expensive operational costs for the transmission and distribution of electricity within the railway system. Quantifying the risk related to vegetation is critical to ensuring service continuity and to reduce maintenance and remediation costs. By carefully surveying and mapping via 3D drone generated models of a power line and surrounding vegetation, inspectors are able to accurately determine the distance between the two.