@inproceedings{6df01ded9a52474caa6adb98c1311b26,
title = "Automatic Training Data Generation Method for Pixel-Level Road Lane Segmentation",
abstract = "Lane detection or road detection is one of the key features of autonomous driving. By using deep convolutional neural network based semantic segmentation, we can build models with high accuracy and robustness. However, training a pixel-level semantic segmentation needs very fine-labeled training data, which requires large amount of labor. In this paper, we propose an automatic training data generating method, which can significantly reduce the effort of the training phase. Experiments prove that our method can generate high-quality training data for lane segmentation task.",
keywords = "Lane detection, Semantic segmentation, Training data generation",
author = "Xun Pan and Yutian Wu and Harutoshi Ogai",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-981-13-5841-8_49",
language = "English",
isbn = "9789811358401",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer-Verlag",
pages = "473--481",
editor = "Shih-Pang Tseng and Bixia Sui and Jeng-Shyang Pan and Lin, {Jerry Chun-Wei}",
booktitle = "Genetic and Evolutionary Computing - Proceedings of the 12th International Conference on Genetic and Evolutionary Computing, 2018",
note = "12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018 ; Conference date: 14-12-2018 Through 17-12-2018",
}