Automatic Training Data Generation Method for Pixel-Level Road Lane Segmentation

Xun Pan, Yutian Wu, Harutoshi Ogai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationGenetic and Evolutionary Computing - Proceedings of the 12th International Conference on Genetic and Evolutionary Computing, 2018
EditorsShih-Pang Tseng, Bixia Sui, Jeng-Shyang Pan, Jerry Chun-Wei Lin
PublisherSpringer-Verlag
Pages473-481
Number of pages9
ISBN (Print)9789811358401
DOIs
Publication statusPublished - 2019 Jan 1
Event12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018 - Changzhou, China
Duration: 2018 Dec 142018 Dec 17

Publication series

NameAdvances in Intelligent Systems and Computing
Volume834
ISSN (Print)2194-5357

Conference

Conference12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018
CountryChina
CityChangzhou
Period18/12/1418/12/17

Keywords

  • Lane detection
  • Semantic segmentation
  • Training data generation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

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  • Cite this

    Pan, X., Wu, Y., & Ogai, H. (2019). Automatic Training Data Generation Method for Pixel-Level Road Lane Segmentation. In S-P. Tseng, B. Sui, J-S. Pan, & J. C-W. Lin (Eds.), Genetic and Evolutionary Computing - Proceedings of the 12th International Conference on Genetic and Evolutionary Computing, 2018 (pp. 473-481). (Advances in Intelligent Systems and Computing; Vol. 834). Springer-Verlag. https://doi.org/10.1007/978-981-13-5841-8_49