Integration of aerial photogrammetry and machine learning in the railway tracks infrastructure monitoring problem

Purpose The purpose of the research was to implement a proper algorithm and identify which one would be the most efficient in data analysis for railway infrastructure monitoring. Design/methodology/approach A couple of algorithms were applied and tested based on the data gathered by the UAV along the railway tracks. The standard methods for assessment were used, and finally, the app was made to show the defined elements of the railway on the map using geolocation. Findings The YOLO algorithm had the best results (ver 8), and finally it was applied in the system. Research limitations/implications The algorithm may be used for a wider purpose if the proper learning methods and tagging of photos and key elements are done. If the UAV delivers an efficient photos (proper speed and distance required) the YOLO algorithm would be an efficient for the track elements identification. Practical implications The railway tracks are currently monitored manually by technicians who have to walk along the tracks. Not only is this not a safe solution, but the reliability and efficiency of this approach are poor. Implementing UAV and AI algorithms for elements identification and preventing the track's faults is a promising solution. The main company PKP wants to implement the solution and scale it up for longer distances. Originality/value Although the AI methods exist, the biggest challenge is to fit them and tag the photos properly so that the process is done efficiently. Applying UAV and AI together to increase the autonomy level is an original approach.