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Lane line detection at nighttime on fractional differential and central line point searching with Fragi and Hessian
To detect lanes at night, each detecting image is the fusion of the multiple images in a video sequence. The valid lane line detection region is identified on region merging. Then, the image preprocessing algorithm based on the Fragi algorithm and Hessian matrix is applied to enhance lanes; to extra...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10182025/ https://www.ncbi.nlm.nih.gov/pubmed/37173300 http://dx.doi.org/10.1038/s41598-022-25032-5 |
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author | Li, Limin Wang, Weixing Wang, Mengfei Feng, Sheng Khatoon, Amna |
author_facet | Li, Limin Wang, Weixing Wang, Mengfei Feng, Sheng Khatoon, Amna |
author_sort | Li, Limin |
collection | PubMed |
description | To detect lanes at night, each detecting image is the fusion of the multiple images in a video sequence. The valid lane line detection region is identified on region merging. Then, the image preprocessing algorithm based on the Fragi algorithm and Hessian matrix is applied to enhance lanes; to extract the lane line center feature points, the image segmentation algorithm based on Fractional differential is proposed; and according to the possible lane line positions, the algorithm detects the centerline points in four directions. Subsequently, the candidate points are determined, and the recursive Hough transformation is applied to obtain the possible lane lines. Finally, to obtain the final lane lines, we assume that one lane line should have an angle between 25 and 65 degrees, while the other should have an angle between 115 and 155 degrees, if the detected line is not in the regions, the Hough line detection will be continued by increasing the threshold value until the two lane lines are got. By testing more than 500 images and comparing deep learning methods and image segmentation algorithms, the lane detection accuracy by the new algorithm is up to 70%. |
format | Online Article Text |
id | pubmed-10182025 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101820252023-05-14 Lane line detection at nighttime on fractional differential and central line point searching with Fragi and Hessian Li, Limin Wang, Weixing Wang, Mengfei Feng, Sheng Khatoon, Amna Sci Rep Article To detect lanes at night, each detecting image is the fusion of the multiple images in a video sequence. The valid lane line detection region is identified on region merging. Then, the image preprocessing algorithm based on the Fragi algorithm and Hessian matrix is applied to enhance lanes; to extract the lane line center feature points, the image segmentation algorithm based on Fractional differential is proposed; and according to the possible lane line positions, the algorithm detects the centerline points in four directions. Subsequently, the candidate points are determined, and the recursive Hough transformation is applied to obtain the possible lane lines. Finally, to obtain the final lane lines, we assume that one lane line should have an angle between 25 and 65 degrees, while the other should have an angle between 115 and 155 degrees, if the detected line is not in the regions, the Hough line detection will be continued by increasing the threshold value until the two lane lines are got. By testing more than 500 images and comparing deep learning methods and image segmentation algorithms, the lane detection accuracy by the new algorithm is up to 70%. Nature Publishing Group UK 2023-05-12 /pmc/articles/PMC10182025/ /pubmed/37173300 http://dx.doi.org/10.1038/s41598-022-25032-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Li, Limin Wang, Weixing Wang, Mengfei Feng, Sheng Khatoon, Amna Lane line detection at nighttime on fractional differential and central line point searching with Fragi and Hessian |
title | Lane line detection at nighttime on fractional differential and central line point searching with Fragi and Hessian |
title_full | Lane line detection at nighttime on fractional differential and central line point searching with Fragi and Hessian |
title_fullStr | Lane line detection at nighttime on fractional differential and central line point searching with Fragi and Hessian |
title_full_unstemmed | Lane line detection at nighttime on fractional differential and central line point searching with Fragi and Hessian |
title_short | Lane line detection at nighttime on fractional differential and central line point searching with Fragi and Hessian |
title_sort | lane line detection at nighttime on fractional differential and central line point searching with fragi and hessian |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10182025/ https://www.ncbi.nlm.nih.gov/pubmed/37173300 http://dx.doi.org/10.1038/s41598-022-25032-5 |
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