Multi road marking detection system for autonomous car using hybrid- based method

For at least two decades, the development of autonomous systems has led to the development of embedded applications allowing to improve the driving comfort and safety. One of the embedded systems that received great attention is road detection system, that operates using road markings detection algo...

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Main Author:	Shah, Khan Bahadur
Format:	Thesis
Language:	English
Published:	2018
Subjects:	Road markings Autonomous vehicles
Online Access:	http://psasir.upm.edu.my/id/eprint/76074/1/FK%202018%20154%20-%20IR.pdf
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id	my-upm-ir.76074
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spelling	my-upm-ir.760742019-12-02T01:22:08Z Multi road marking detection system for autonomous car using hybrid- based method 2018-04 Shah, Khan Bahadur For at least two decades, the development of autonomous systems has led to the development of embedded applications allowing to improve the driving comfort and safety. One of the embedded systems that received great attention is road detection system, that operates using road markings detection algorithm. To date, the issue on detecting road markings under various imaging conditions has not been tackled yet. Generally, the road markings detection is performed on road images extracted from videos that were recorded using a camera, which was placed inside a vehicle at a fixed position. In this thesis, a road markings detection system that tackle the problems of detecting road markings under various weather and illumination conditions is proposed. The proposed system consists of a combination of Inverse Perspective Transform method, an image enhancement method and edge detection method. The Inverse Perspective Transform method was used to convert images, which were extracted from the recorded videos to bird’s-eye view images, while an image enhancement method, namely Contrast Limited Adaptive Histogram Equalization (CLAHE) was used to tackle various illumination conditions and Sobel edge detection method for detecting the road markings. Experimented on Large Variability Road Images database (LVRI) that consists of 22,500 road images, which were extracted from videos recorded around Selangor and Kuala Lumpur and T. Wu dataset that consist of 1208 road images, which were extracted from videos recorded around California, the proposed algorithm performed satisfactorily. With an accuracy of 96.53% using LVRI and 99.33% using the T. Wu datasets, the proposed algorithm able to detect almost all types of road markings. The types of road markings available in the LVRI and T. Wu datasets are forward arrow, left-side arrow, right-side arrow, lanes and signs printed on the road that are under various imaging conditions, including complex background and occlusion. In addition, the proposed algorithm outperformed the algorithm introduced by T. Wu. However, the algorithm has difficulty in detecting road markings painted in soft yellow color. Hence, in future, the algorithm will be improved by incorporating HSI color analysis with the aim of tackling the problem of detecting road markings that are painted in soft yellow color. Road markings Autonomous vehicles 2018-04 Thesis http://psasir.upm.edu.my/id/eprint/76074/ http://psasir.upm.edu.my/id/eprint/76074/1/FK%202018%20154%20-%20IR.pdf text en public masters Universiti Putra Malaysia Road markings Autonomous vehicles
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Road markings Autonomous vehicles
spellingShingle	Road markings Autonomous vehicles Shah, Khan Bahadur Multi road marking detection system for autonomous car using hybrid- based method
description	For at least two decades, the development of autonomous systems has led to the development of embedded applications allowing to improve the driving comfort and safety. One of the embedded systems that received great attention is road detection system, that operates using road markings detection algorithm. To date, the issue on detecting road markings under various imaging conditions has not been tackled yet. Generally, the road markings detection is performed on road images extracted from videos that were recorded using a camera, which was placed inside a vehicle at a fixed position. In this thesis, a road markings detection system that tackle the problems of detecting road markings under various weather and illumination conditions is proposed. The proposed system consists of a combination of Inverse Perspective Transform method, an image enhancement method and edge detection method. The Inverse Perspective Transform method was used to convert images, which were extracted from the recorded videos to bird’s-eye view images, while an image enhancement method, namely Contrast Limited Adaptive Histogram Equalization (CLAHE) was used to tackle various illumination conditions and Sobel edge detection method for detecting the road markings. Experimented on Large Variability Road Images database (LVRI) that consists of 22,500 road images, which were extracted from videos recorded around Selangor and Kuala Lumpur and T. Wu dataset that consist of 1208 road images, which were extracted from videos recorded around California, the proposed algorithm performed satisfactorily. With an accuracy of 96.53% using LVRI and 99.33% using the T. Wu datasets, the proposed algorithm able to detect almost all types of road markings. The types of road markings available in the LVRI and T. Wu datasets are forward arrow, left-side arrow, right-side arrow, lanes and signs printed on the road that are under various imaging conditions, including complex background and occlusion. In addition, the proposed algorithm outperformed the algorithm introduced by T. Wu. However, the algorithm has difficulty in detecting road markings painted in soft yellow color. Hence, in future, the algorithm will be improved by incorporating HSI color analysis with the aim of tackling the problem of detecting road markings that are painted in soft yellow color.
format	Thesis
qualification_level	Master's degree
author	Shah, Khan Bahadur
author_facet	Shah, Khan Bahadur
author_sort	Shah, Khan Bahadur
title	Multi road marking detection system for autonomous car using hybrid- based method
title_short	Multi road marking detection system for autonomous car using hybrid- based method
title_full	Multi road marking detection system for autonomous car using hybrid- based method
title_fullStr	Multi road marking detection system for autonomous car using hybrid- based method
title_full_unstemmed	Multi road marking detection system for autonomous car using hybrid- based method
title_sort	multi road marking detection system for autonomous car using hybrid- based method
granting_institution	Universiti Putra Malaysia
publishDate	2018
url	http://psasir.upm.edu.my/id/eprint/76074/1/FK%202018%20154%20-%20IR.pdf
_version_	1747813112991449088

Multi road marking detection system for autonomous car using hybrid- based method

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