Automatic pavement distress severity detection using deep learning
Parisa Setayesh Valipour, Amir Golroo, Afarin Kheirati, Mohammadsadegh Fahmani, Mohammad Javad Amani
Abstract
Roads are one of the most critical infrastructures, which should be maintained at a high quality of service. For this purpose, road pavement should be assessed cost-effectively. In the past, image processing methods were used to analyze pavement conditions. In recent years, machine learning methods have been employed, while now deep learning methods are applied. Deep learning has outperformed other methods regarding the accuracy and speed of pavement distress evaluation. In this research, a deep learning algorithm called YOLOv5 is deployed to detect pavement block cracking and estimate its severity using images taken from the right of way via a road surface profiler. Two models are successfully trained and tested, one to detect block cracking and the other to predict its severity with a sufficient level of accuracy of 84.5% and 76.6%, respectively. It is concluded that the model not only can detect block cracking but also predict its severity.