Pavement damage identification and evaluation in UAV-captured images using gray level co-occurrence matrix and cloud model

Abstract

In the road structure, the pavement is in direct contact with the wheel, and its daily maintenance is very important. Traditional pavement damage detection relies heavily on manual work, which is inefficient and unsafe. In this paper, the unmanned aerial vehicle (UAV) is used to pick up the pavement damage image, and then combine the gray level co-occurrence matrix (GLCM) algorithm and the cloud model theory to construct a damage identification and evaluation model. In the experimental stage, a road in Chongqing, China is selected to verify the model. The results show that the accuracy of the proposed theoretical model in pavement damage identification exceeds 82.5%, and the performance is good. In addition, in the evaluation case, the lightest damage is the crack, and the most serious is the rut. For the hollow and block repair, the texture feature cloud droplet membership ratio of the two is mostly between 0.5 and 0.6, indicating that the damage state of the pavement is evolving from slight to moderate.