Line-Structured Light Rut Detection of Asphalt Pavement with Pavement Markings Interference under Strong Light

The construction of highways has been well-developed worldwide. Meanwhile, the heavy traffic flow brings huge pressure on highway maintenance. Pavement rutting is one of the major pavement distresses and its detection has been a research hot spot in pavement engineering. Despite the fruitful research outcomes, most of them were based on ideal circumstances and focused on how to improve the processing procedure to reduce the detection error of usual rutting measurement. Whereas some particular interference, such as pavement markings under strong light, usually occurs during the detection, and remains undetected. Pavement markings affect the accurate extraction of pavement transverse profiles and increase the detection error of rut depth. To fill this gap, this study proposed a line-structured rut detection method to improve the detecting accuracy of rut depth. The global gray scale correction algorithm and feature-based fusion segmentation algorithm are mainly used to eliminate pavement markings of the background. The centerline-based midpoint thinning algorithm, least square based curve correction method, and envelope model are applied to calculate the rut depth, and are applicable for different forms of rutting distress. A total of 600 of images collected from urban roads were classified into four categories and used to verify the proposed rut detection method with pavement markings interference under strong light. The experimental results indicate that the average relative detection error is 10.07% and the average proportion of detection accuracy is 87.65%. Meanwhile, the evaluation accuracy of the pavement condition assessed by the rut depth index reaches 83.87%. This manifests that the proposed method can not only deal with the rutting detection with interference, but can also apply to the situation without interference. Thus, the method could be used to evaluate pavement condition and offer a reliable data source for pavement maintenance. The work in the paper offers a vital reference for pavement rut detection methods worldwide.