Pavement Surface Type Classification Based on Deep Learning to the Automatic Pavement Evaluation System

Resumo

Computer vision techniques, image processing, and machine learning became incorporated into an
automatic pavement evaluation system with technological advances. However, in most research, the models
developed to identify defects in the pavement assume that all the segments evaluated are paved and with one
specific pavement surface type. Nevertheless, there is a wide variety of road surface types, especially in urban
areas. The present work developed models based on a deep convolutional neural network to identify the pavement
surface types considering five classes: asphalt, concrete, interlocking, cobblestone, and unpaved. Models based on
ResNet50 architectures were developed; also, the Learning Rate (LR) optimization “one-cycle” training technique
was applied. The models were trained using almost 50 thousand images from Brazil’s states highway dataset.
model results are excellent, highlighting the model based on ResNet50, in which it obtained accuracy, precision,
and recall values of almost 100%.