Identification and classification of structural defects in the road network using a global scale automatic road defect detection system

Road networks are created with different pavement designs. In general, flexible pavement is preferred as the most used superstructure type in the World. Road networks with this superstructure suffers from degradation due to these; due to reasons such as lack of proper road infrastructure, incompatibility of bitumen used in bituminous mixtures with the application area, errors in aggregate bitumen ratio, casting temperature, insufficient roller operation, unqualified personnel, seasonal variations, methods of combating snow and ice, traffic volume, inadequate maintenance and repair processes. These degradations take different forms over time and create road defects. The process of identifying defects is very important for the efficiency of the road pavement and traffic components. In this thesis study, the process of automatic detection and classification of road defects occurring in flexible superstructure road networks was carried out with an intelligent system. Road images obtained from eight different countries and 10 different road defects were taken into consideration within the scope of the study. YOLOv5, YOLOv7 and YOLOv8 detection models were used in the detection process of defects. In addition, a global-scale automatic road defect detection system has been developed within the scope of a data set consisting of different countries. The developed system is a prototype and has the ability to detect and classify defects occurring in road networks in real time. It is clear that the system will be a guide for road network managers by obtaining location information of the identified defects. Detecting, classifying and locating road defects with the developed system will accelerate the maintenance and repair process of road networks and also extend their service life. Road safety and comfort of traffic components using the road network will also be increased. As a result, an example of vehicle-infrastructure (V2I) communication, which is a form of intelligent transportation systems application, is presented within the scope of the thesis.