Automated Distress Detection, Classification and Measurement for Asphalt Urban Pavements Using YOLO

Abstract

In pavement management, it is essential to have a good database with information on the condition of the roads that compose the corresponding network. In Chile, such a database does not currently exist, and there is no technology that can evaluate urban pavement condition in an efficient way. On this research, more than 50,000 images of 13.2 × 2.6 m of asphalt pavement from different zones of Santiago, Chile, were obtained. These images were processed, and the following distresses were labeled with two different levels of severities: patches; potholes; and transversal, longitudinal, and fatigue cracking. These data were used to train and evaluate the following object detection convolutional neural network models: YOLOv5 and YOLOv7.

Keywords

• asphalt pavement
• urban pavements
• distress detection
• distress classification
• deep learning
• convolutional neural network