Robust road lane detection using extremal-region enhancement

Jingchen Gu, Qieshi Zhang, Seiichiro Kamata

研究成果: Conference contribution

2 引用 (Scopus)

抜粋

Road lane detection is a key problem in advanced driver-assistance systems (ADAS). For solving this problem, vision-based detection methods are widely used and are generally focused on edge information. However, only using edge information leads to miss detection and error detection in various road conditions. In this paper, we propose a neighbor-based image conversion method, called extremal-region enhancement. The proposed method enhances the white lines in intensity, hence it is robust to shadows and illuminance changes. Both edge and shape information of white lines are extracted as lane features in the method. In addition, we implement a robust road lane detection algorithm using the extracted features and improve the correctness through probability tracking. The experimental result shows an average detection rate increase of 13.2% over existing works.

元の言語English
ホスト出版物のタイトルProceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015
出版者Institute of Electrical and Electronics Engineers Inc.
ページ519-523
ページ数5
ISBN(電子版)9781479961009
DOI
出版物ステータスPublished - 2016 6 7
イベント3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015 - Kuala Lumpur, Malaysia
継続期間: 2016 11 32016 11 6

Other

Other3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015
Malaysia
Kuala Lumpur
期間16/11/316/11/6

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • これを引用

    Gu, J., Zhang, Q., & Kamata, S. (2016). Robust road lane detection using extremal-region enhancement. : Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015 (pp. 519-523). [7486557] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACPR.2015.7486557