Pavement Surface Condition Rating of Flexible Pavement based on Artificial Intelligence based Deep Learning Technique

Developing countries were struggling to maintain its road networks in fit surface conditions due to adherence of the conventional method of evaluating the pavement surface condition using multiple physical equipment. The conventional method to measure the multiple distresses is a time consuming and costlier process. Hence in this research, we propose a deep learning-based Pavement Surface Condition Rating of plastic roads. The proposed hybrid model was adopted to assess the pavement surface condition assessment aligned to Pavement condition rating recommended by IRC 82:2015 used in pavement maintenance of Indian roads. In this research, 4k video image of pavement having distresses such as crack, potholes, patches and raveling are acquired by unmanned aerial vehicle mounted with camera. The collected video dataset was used to develop distress prediction model using YOLOv8 integrated with Gray-Level Co-occurrence Matrix (GLCM) and TAMURA feature extraction algorithm. It is observed from the results that the pavement condition rating based on conventional and deep learning- based Image processing technique has high degree of correlation.