@inproceedings{e04bbf3ebe754277a2228e8ddbe14810,
title = "Vision-based pavement marking detection : a case study",
abstract = "Pavement markings take responsibility to communicate with road users regarding travel regulations and guidance. Due to their irreplaceable role in ensuring the safety and order on road, it would be beneficial for road agencies to keep an as-is inventory record of the pavement markings on their roads for managerial operations. However, faced with the sheer volume of their responsible assets, manual inspection would be time-consuming and error prone. This study proposes a vision-based method to automatically detect and classify longitudinal markings using videos of road pavement. Not only line markings, audible markings, as a special category, were also identified in the images. The proposed method can achieve an average 0.89 detection accuracy for line markings and 0.82 for audible markings. Limitations and future work are also proposed. This study tests the possibility of utilising visual data to assist road agencies with an informative management of their civil assets.",
keywords = "computer vision, pavements, road markings, roads",
author = "Shuyuan Xu and Jun Wang and Peng Wu and Wenchi Shou and Tingchen Fang and Xiangyu Wang",
year = "2020",
doi = "10.1007/978-3-030-51295-8_81",
language = "English",
isbn = "9783030512941",
publisher = "Springer",
pages = "1162--1172",
booktitle = "Proceedings of the 18th International Conference on Computing in Civil and Building Engineering (ICCCBE 2020), 18 - 20 August, 2020, S{\~A}£o Paulo, Brazil",
note = "International Conference on Computing in Civil and Building Engineering ; Conference date: 18-08-2020",
}