Vision-Based Recognition of Road Regulation for Intelligent Vehicle

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

doi:10.1109/IVS.2018.8500550

Vision-Based Recognition of Road Regulation for Intelligent Vehicle

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

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 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 language	English
Title of host publication	2018 IEEE Intelligent Vehicles Symposium, IV 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1418-1425
Number of pages	8
ISBN (Electronic)	9781538644522
DOIs	https://doi.org/10.1109/IVS.2018.8500550
Publication status	Published - 2018 Oct 18
Event	2018 IEEE Intelligent Vehicles Symposium, IV 2018 - Changshu, Suzhou, China Duration: 2018 Sept 26 → 2018 Sept 30

Publication series

Name	IEEE Intelligent Vehicles Symposium, Proceedings
Volume	2018-June

Other

Other	2018 IEEE Intelligent Vehicles Symposium, IV 2018
Country/Territory	China
City	Changshu, Suzhou
Period	18/9/26 → 18/9/30

Bibliographical note

Funding Information:
This work was partly supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No. 2016-0-00152, Development of Smart Car Vision Techniques based on Deep Learning for Pedestrian Safety) and MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2018-2016-0-00464) supervised by the IITP(Institute for Information & communications Technology Promotion).

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

Computer Science Applications
Automotive Engineering
Modelling and Simulation

Access to Document

10.1109/IVS.2018.8500550

Cite this

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title = "Vision-Based Recognition of Road Regulation for Intelligent Vehicle",

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.",

author = "Kwangyong Lim and Yongwon Hong and Minsong Ki and Yeongwoo Choi and Hyeran Byun",

note = "Funding Information: This work was partly supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No. 2016-0-00152, Development of Smart Car Vision Techniques based on Deep Learning for Pedestrian Safety) and MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2018-2016-0-00464) supervised by the IITP(Institute for Information & communications Technology Promotion). Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Intelligent Vehicles Symposium, IV 2018 ; Conference date: 26-09-2018 Through 30-09-2018",

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doi = "10.1109/IVS.2018.8500550",

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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., 8500550, IEEE Intelligent Vehicles Symposium, Proceedings, vol. 2018-June, Institute of Electrical and Electronics Engineers Inc., pp. 1418-1425, 2018 IEEE Intelligent Vehicles Symposium, IV 2018, Changshu, Suzhou, China, 18/9/26. https://doi.org/10.1109/IVS.2018.8500550

Vision-Based Recognition of Road Regulation for Intelligent Vehicle. / Lim, Kwangyong; Hong, Yongwon; Ki, Minsong et al.

2018 IEEE Intelligent Vehicles Symposium, IV 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1418-1425 8500550 (IEEE Intelligent Vehicles Symposium, Proceedings; Vol. 2018-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - 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.

AB - 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.

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Vision-Based Recognition of Road Regulation for Intelligent Vehicle

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