Smart monitoring of road pavement deformations from UAV images by using machine learning

Abstract

Road pavement deformation monitoring is considered the main task for maintenance purposes, especially potholes and cracks, which are the most common types of road deformation surfaces. In order to make pavement inspections more effective, new types of remote sensing data that do not damage the pavement are being used more and more to find pavement distress. This article presents a proposed approach for extracting surface cracks from unmanned aerial vehicle (UAV) images using machine learning, focusing on the data pre-treatment processes. The objective of this study is to evaluate the effectiveness of decision tree classification (DT) in detecting cracks. The performance of the models is also evaluated. The performance evaluation approach is predicated on two primary criteria: model validation and testing. Also, the extent of the impact of post-classification operations, edge detection technology, and morphological processes on crack identification as well as classification accuracy, the digital orthomosaic was generated by the use of a technique commonly referred to as backward projection. To achieve this, the study uses a fusion of gray-level co-occurrence matrix (GLCM) attribute data and RGB images. Cracks are discovered using a classification tree (CT)-based classification approach with an overall classification rate of 86%. Ultimately, morphological processes using the closed image that was formed had a commendable level of accuracy, with an overall classification rate of 96%. The Canny edge detection algorithm has demonstrated its efficacy as a preferred method for detecting cracks from UAV images, providing invaluable decision support for actual road maintenance.