@inproceedings{47ec038916704f73aa9924e52ff2709e,
title = "RLMD: A Dataset for Road Marking Segmentation",
abstract = "Road marking recognition is an important task for advanced driver assistance systems and autonomous vehicle. In the existing research, most techniques focus on detection mainly due to the lack of segmentation datasets. The segmentation of road markings can be used to understand the traffic regulations, as well as for vehicle localization. This paper introduces a road marking segmentation dataset, RLMD. It consists of 700 images annotated with 25 categories. The effectiveness of our dataset is evaluated using state-of-the-art segmentation techniques. The performance comparison is performed with well-known public datasets, Bdd100k and CeyMo. The dataset and code are made available publicly at https://github.com/stu9113611/RLMD.",
author = "Hsiao, {Heng Chih} and Cai, {Yi Chang} and Lin, {Huei Yung} and Chiu, {Wei Chen} and Chan, {Chiao Tung}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 ; Conference date: 17-07-2023 Through 19-07-2023",
year = "2023",
doi = "10.1109/ICCE-Taiwan58799.2023.10226935",
language = "English",
series = "2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "427--428",
booktitle = "2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings",
address = "美國",
}