Evaluating Pavement Lane Markings in Metropolitan Road Networks with a Vehicle-Mounted Retroreflectometer and AI-Based Image Processing Techniques

Abstract

The objectives of this study were to evaluate pavement lane markings in a metropolitan road network and to develop a maintenance strategy for safe daytime and night-time driving. To achieve this, data on the retroreflectivity and physical defect ratio of lane markings were collected remotely using a vehicle-mounted retroreflectometer and high-resolution camera. The retroreflectivity was measured and analyzed by road type (city freeways, arterial roads, and collector roads) and by lane color (yellow, white, and blue) over a total length of 6790.34 km. The results indicate that the retroreflective performance deteriorates the most in the case of white lanes, regardless of the road classification, especially in the case of the first white lane. Additionally, the physical defects of lane markings were investigated over a total length of 502.82 km and categorized by road classification and lane color. Mask R-CNN and the Otsu Threshold method were used to automatically calculate the ratios of the defects. The results indicate that city freeways show a lower defect ratio than arterial and collector roads for all colors. Moreover, there is no significant difference between the white lanes for all types of roads. The distribution trends and relationship between retroreflectivity and the defect ratios were discussed according to the road type and lane color, and a method for selecting maintenance priority was suggested. The results show that the number of lanes requiring the restoration of retroreflectivity increases as the defect ratio increases. Therefore, we suggest prioritizing maintenance work on the lanes with a higher ratio of defects, covering a higher proportion of low-retroreflectivity sections. In addition, the unit length for data averaging can be adjusted to improve the work efficiency.