Automatic Classification and Quantification of Basic Distresses on Urban Flexible Pavement through Convolutional Neural Networks

Pavement condition assessment is a critical step in road pavement management. In contrast to the automatic and objective methods used for rural roads, most commonly method urban areas development of visual surveys usually filled out by technicians, which leads subjective assessment. Whereas previous studies on identification distresses focused crack detection, this research aims not only cover classification multiple flexible (longitudinal transverse cracking, alligator raveling, potholes, patching), but also quantify them through application convolutional neural networks. Additionally, study proposes methodology an considering different stages developed research. This allows more efficient reliable assessment, minimizing cost time required current surveys.