Detection of potholes on road surfaces using photogrammetry and remote sensing methods (review)

An overview of methods for obtaining 2D and 3D models of defects on the pavement is given. The integrity of the pavement can be affected by factors such as temperature, humidity, weathering and loads. Potholes are one of the most common types of pavement failure. These defects are the signs of structural failures in an asphalt road. The process of collecting and analyzing data is critical to pavement maintenance. Finding and quantifying pothole geometry information is essential to understand road maintenance forecasts and to determine the right asphalt maintenance strategies. Visual detection of road defects is costly and time consuming. Today, there are quite a lot of studies in the scientific literature showing methods for automatic detection and recognition of potholes. In our work, we consider methods for automatic detection and classification of potholes using tools — sensors integrated with a positioning system. The technique of processing two-dimensional (2D) images using various methods of machine classification allows you to determine the precise geometry of the pothole. Algorithmic methods such as artificial neural networks, decision trees, support vector machines, and fuzzy classification are used to improve the accuracy of image processing and highlight the edges of potholes. A three-dimensional model of the pothole (3D) can be obtained based on laser scanning data and photogrammetry methods. The paper summarizes various methods and proposed techniques for extracting a 3D pothole model. The results of the work can be used to improve the infrastructure for maintaining road surfaces.