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A real time road marking detection system on large variability road images database

For no less than two decades, the development of autonomous systems has led to the development of embedded applications permitting to enhance the driving comfort and limit the hazard level of dangerous zones. One of the first embedded system is a lane detection system, which was implemented using ro...

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Main Authors: Khan, Bahadur Shah, Hanafi, Marsyita, Mashohor, Syamsiah
Format: Conference or Workshop Item
Language:English
Published: Springer 2017
Online Access:http://psasir.upm.edu.my/id/eprint/15025/1/A%20real%20time%20road%20marking%20detection%20system%20on%20large%20variability%20road%20images%20database.pdf
http://psasir.upm.edu.my/id/eprint/15025/
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spelling my.upm.eprints.150252019-05-08T07:49:48Z http://psasir.upm.edu.my/id/eprint/15025/ A real time road marking detection system on large variability road images database Khan, Bahadur Shah Hanafi, Marsyita Mashohor, Syamsiah For no less than two decades, the development of autonomous systems has led to the development of embedded applications permitting to enhance the driving comfort and limit the hazard level of dangerous zones. One of the first embedded system is a lane detection system, which was implemented using road marking detection algorithms with the aim to produce a system that is able to detect various shapes of road markings on the images that are captured under various imaging conditions. Generally, the road images were captured using a camera, which has been placed inside a vehicle at a fixed position. In this paper, a road markings detection system that tackles the problems of detecting road markings on the images captured under various weather and illumination conditions is proposed. The proposed system consists of inverse perspective transform method, which is used to convert an image into a bird’s-eye view image, an image normalization method, namely CLAHE that tackle various illumination conditions and Sobel edge detection method for identifying the road marker. We demonstrate the usefulness of the constructed algorithm by performing experiments on our Large Variability Road Images database (LVRI) that consists of 22,500 road images with the accuracy of 96.53%. Springer 2017 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/15025/1/A%20real%20time%20road%20marking%20detection%20system%20on%20large%20variability%20road%20images%20database.pdf Khan, Bahadur Shah and Hanafi, Marsyita and Mashohor, Syamsiah (2017) A real time road marking detection system on large variability road images database. In: 4th International Conference on Computational Science and Technology (ICCST 2017), 29-30 Nov. 2017, Kuala Lumpur, Malaysia. (pp. 31-41). 10.1007/978-981-10-8276-4_4
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description For no less than two decades, the development of autonomous systems has led to the development of embedded applications permitting to enhance the driving comfort and limit the hazard level of dangerous zones. One of the first embedded system is a lane detection system, which was implemented using road marking detection algorithms with the aim to produce a system that is able to detect various shapes of road markings on the images that are captured under various imaging conditions. Generally, the road images were captured using a camera, which has been placed inside a vehicle at a fixed position. In this paper, a road markings detection system that tackles the problems of detecting road markings on the images captured under various weather and illumination conditions is proposed. The proposed system consists of inverse perspective transform method, which is used to convert an image into a bird’s-eye view image, an image normalization method, namely CLAHE that tackle various illumination conditions and Sobel edge detection method for identifying the road marker. We demonstrate the usefulness of the constructed algorithm by performing experiments on our Large Variability Road Images database (LVRI) that consists of 22,500 road images with the accuracy of 96.53%.
format Conference or Workshop Item
author Khan, Bahadur Shah
Hanafi, Marsyita
Mashohor, Syamsiah
spellingShingle Khan, Bahadur Shah
Hanafi, Marsyita
Mashohor, Syamsiah
A real time road marking detection system on large variability road images database
author_facet Khan, Bahadur Shah
Hanafi, Marsyita
Mashohor, Syamsiah
author_sort Khan, Bahadur Shah
title A real time road marking detection system on large variability road images database
title_short A real time road marking detection system on large variability road images database
title_full A real time road marking detection system on large variability road images database
title_fullStr A real time road marking detection system on large variability road images database
title_full_unstemmed A real time road marking detection system on large variability road images database
title_sort real time road marking detection system on large variability road images database
publisher Springer
publishDate 2017
url http://psasir.upm.edu.my/id/eprint/15025/1/A%20real%20time%20road%20marking%20detection%20system%20on%20large%20variability%20road%20images%20database.pdf
http://psasir.upm.edu.my/id/eprint/15025/
_version_ 1643825809717198848
score 13.211869

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