Automated Pavement Distress Detection, Classification and Measurement: A Review

Road surface distress is an unavoidable situation due to age, vehicles overloading, temperature changes, etc. In the beginning, pavement maintenance actions took only place after having too much damage, which leads costly corrective actions. Therefore, scheduled road inspections can extend service life while guaranteeing users security and comfort. Traditional manual visual don’t meet nowadays criteria, in addition a relatively high time volume consumption. Smart City management preventive approach requires accurate scalable data deduce significant indicators plan efficient programs. However, quality of depends on sensors used conditions during scanning. Many studies focused different sensors, Machine Learning algorithms Deep Neural Networks tried find sustainable solution. Besides all these studies, measurement stills challenge Smarts Cities because detection not enough decide required. Damages localization, dimensions future development should be highly detected real-time. This paper summarizes state-of-the-art methods technologies recent years detection, classification measurement. The aim evaluate current highlight their limitations, lay out blueprint for researches. PMS (Pavement Management System) automated monitoring with accuracy large networks.