Abstract
The primary objective of this work is the automatic analysis of road marking degradation, to support maintenance decision making. The evaluation is conducted in two main aspects: the visual condition, assessed using a camera, and the retroreflective quality, which ensures nighttime visibility and is evaluated using LiDAR data. We employ the latest YOLOv12 neural network for road marking detection, enabling real-time analysis during vehicle movement. Following detection, LiDAR data is recorded and used to analyze the quality of the reflected beams. The primary parameter considered in this analysis is the intensity of the reflected signal (ranging from 0 to 255). Based on this parameter, road markings are classified into two categories: good condition and those requiring maintenance. The proposed approach provides an automated and effective tool for road infrastructure monitoring within intelligent transportation systems.
Paper Type
Poster
DOI
10.62036/ISD.2025.148
Automated Evaluation of Pavement Marking Quality Based on Multi-Sensor Data
The primary objective of this work is the automatic analysis of road marking degradation, to support maintenance decision making. The evaluation is conducted in two main aspects: the visual condition, assessed using a camera, and the retroreflective quality, which ensures nighttime visibility and is evaluated using LiDAR data. We employ the latest YOLOv12 neural network for road marking detection, enabling real-time analysis during vehicle movement. Following detection, LiDAR data is recorded and used to analyze the quality of the reflected beams. The primary parameter considered in this analysis is the intensity of the reflected signal (ranging from 0 to 255). Based on this parameter, road markings are classified into two categories: good condition and those requiring maintenance. The proposed approach provides an automated and effective tool for road infrastructure monitoring within intelligent transportation systems.
Recommended Citation
Kulawik, J. & Karbowiak, L. (2025). Automated Evaluation of Pavement Marking Quality Based on Multi-Sensor DataIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.148