Vision-Based Recognition of Road Regulation for Intelligent Vehicle

Kwangyong Lim, Yongwon Hong, Minsong Ki, Yeongwoo Choi, Hyeran Byun

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

3 Citations (Scopus)

Abstract

In this paper, we present a new framework to detect and recognize entire lanes and symbolic marks on high resolution road images. The first part of the framework utilizes local threshold to overcome the limitations of fixed threshold determination in road marking segmentation. The second part of the framework handles false detections caused by nearby objects on the roads such as vehicles and buildings by re-moving the areas that are not related to road surface using semantic segmentation. It also boosts recognition performance with a cascaded classifier structure that combines CNN for symbolic mark recognition and SVM for lane verification. The proposed lane detection achieves average Fl-score of 0.96 and symbol recognition achieves average Fl-score of 0.91. The proposed method is expected to advance the vehicle industry; with a GPU device, the proposed method can easily be embedded in smart vehicles.

Original languageEnglish
Title of host publication2018 IEEE Intelligent Vehicles Symposium, IV 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1418-1425
Number of pages8
ISBN (Electronic)9781538644522
DOIs
Publication statusPublished - 2018 Oct 18
Event2018 IEEE Intelligent Vehicles Symposium, IV 2018 - Changshu, Suzhou, China
Duration: 2018 Sep 262018 Sep 30

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings
Volume2018-June

Other

Other2018 IEEE Intelligent Vehicles Symposium, IV 2018
CountryChina
CityChangshu, Suzhou
Period18/9/2618/9/30

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All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Automotive Engineering
  • Modelling and Simulation

Cite this

Lim, K., Hong, Y., Ki, M., Choi, Y., & Byun, H. (2018). Vision-Based Recognition of Road Regulation for Intelligent Vehicle. In 2018 IEEE Intelligent Vehicles Symposium, IV 2018 (pp. 1418-1425). [8500550] (IEEE Intelligent Vehicles Symposium, Proceedings; Vol. 2018-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IVS.2018.8500550