Crack Auscultation in Asphalt Pavements Using Computer Vision

Auscultation of cracks in asphalt pavements plays a fundamental role for ensuring transportation infrastructure maintenance and longevity. This paper presents a system that combines neural network algorithms to detect, classify, and segment pavement cracks in order to produce reports of the concentration of cracks in asphalt pavements. The proposed approach generates a choropleth map that allows road inspectors to quickly determine the state of the pavement and get more information on the type of cracks present on the pavement. By implementing this system, continuous auscultation of asphalt pavements becomes feasible, contributing to effective infrastructure management and maintenance practices.