Development of an Algorithm for Analysing the Condition of the Road Surface Using Artificial Intelligence

Motorways are a strategically important part of a country’s infrastructure. High requirements for their condition stipulate regular monitoring of quality of the road surface. The large length of motorways in Russia and the impact of weather and climate conditions (seasonal temperature fluctuations, precipitation) emphasise the relevance of searching for non-destructive testing methods for road diagnostics that ensure short terms of diagnostic work and the use of minimal resources. The considered existing solutions for detecting road surface damage include the use of ground penetrating radar, laser method, method of analysing vibration effects of road surface irregularities, detection of damage based on lidar data and mobile mapping systems. The objective of the study was to develop an algorithm for analysing the condition of the road surface that allows detecting road surface distresses based on images obtained during the diagnostics of motorways by the KP-514-RDT airfield and road measuring mobile laboratory completed by the IndorRoad and RDT-Line software packages. The development of an algorithm for detecting road surface defects was carried out using machine learning methods. The detected defects have precise georeferencing according to stationing of the measured road segment. As a result of development, a trained model was obtained that allows automatic marking of defects of different classes on the image. The developed algorithm is integrated into the software for managing the monitoring of the condition of regional and municipal roads.