Damaged lane markings detection method with label propagation

Tetsuo Nukita, Yasunari Kishimoto, Yasuhiro Iida, Makoto Kawano, Takuro Yonezawa, Jin Nakazawa

研究成果: Conference contribution

抄録

We propose a damaged traffic lane detection method ensuring high accuracy in spite of only a few number of supervised data which are labeled traffic lane images. In general, supervised machine learning approach is very powerful for image classification. However, preparing a large amount of supervised data is time-consuming task because labeling damaged or not damaged is usually done manually through visual inspection of images. Thus, lowering the cost of labeling data is a great concern. To this end, we adopt a semi-supervised machine learning approach which learns from both labeled and unlabeled data by constructing graph based on the image similarity. We captured a large amount of the road lane images. Then, we constructed graph structure whose nodes are the road lane images and whose edges are the similarity between the images. In several nodes, we assigned labels which denote "damaged" or "not damaged." Finally we utilized the label propagation, which made it possible to infer the labels of the unlabeled data from the labeled data. These estimation resulted in the accuracy rate over 85% from the supervised data, which accounted for only 1.8% of the total data.

本文言語English
ホスト出版物のタイトルProceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ203-208
ページ数6
ISBN(電子版)9781538677599
DOI
出版ステータスPublished - 2019 1 9
イベント24th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018 - Hakodate, Japan
継続期間: 2018 8 292018 8 31

出版物シリーズ

名前Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018

Conference

Conference24th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018
CountryJapan
CityHakodate
Period18/8/2918/8/31

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

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