A computer vision-based method to identify the international roughness index of highway pavements

The International Roughness Index (IRI) is one of the most critical parameters in the field of pavement performance management. Traditional methods for the measurement of IRI rely on expensive instrumented vehicles and well-trained professionals. The equipment and labor costs of traditional measurement methods limit the timely updates of IRI on the pavements. In this article, a novel imaging-based Deep Neural Network (DNN) model, which can use pavement photos to directly identify the IRI values, is proposed. This model proved that it is possible to use 2-dimensional (2D) images to identify the IRI other than the typically used vertical accelerations or 3-dimensional (3D) images. Due to the fast growth in photography equipment, small and convenient sports action cameras such as the GoPro Hero series are able to capture smooth videos at a high framerate with built-in electronic image stabilization systems. These significant improvements make it not only more convenient to collect high-quality 2D images, but also easier to process them than vibrations or accelerations. In the proposed method, 15% of the imaging data were randomly selected for testing and had never been touched during the training steps. The testing results showed an averaged coefficient of determination (R square) of 0.6728 and an averaged root mean square error (RMSE) of 0.50.