基于视觉的多阶段车道检测算法

IF 0.6 Q3 MULTIDISCIPLINARY SCIENCES Pertanika Journal of Science and Technology Pub Date : 2024-07-22 DOI:10.47836/pjst.32.4.19
Fayez Saeed Faizi, A. K. Al-Sulaifanie
{"title":"基于视觉的多阶段车道检测算法","authors":"Fayez Saeed Faizi, A. K. Al-Sulaifanie","doi":"10.47836/pjst.32.4.19","DOIUrl":null,"url":null,"abstract":"Lane detection is an essential task for autonomous vehicles. Deep learning-based lane detection methods are leading development in this sector. This paper proposes an algorithm named Deep Learning-based Lane Detection (DLbLD), a Convolutional Neural Network (CNN)-based lane detection algorithm. The presented paradigm deploys CNN to detect line features in the image block, predict a point on the lane line part, and project all the detected points for each frame into one-dimensional form before applying K-mean clustering to assign points to related lane lines. Extensive tests on different benchmarks were done to evaluate the performance of the proposed algorithm. The results demonstrate that the introduced DLbLD scheme achieves state-of-the-art performance, where F1 scores of 97.19 and 79.02 have been recorded for TuSimple and CU-Lane benchmarks, respectively. Nevertheless, results indicate the high accuracy of the proposed algorithm.","PeriodicalId":46234,"journal":{"name":"Pertanika Journal of Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Vision-Based Multi-Stages Lane Detection Algorithm\",\"authors\":\"Fayez Saeed Faizi, A. K. Al-Sulaifanie\",\"doi\":\"10.47836/pjst.32.4.19\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Lane detection is an essential task for autonomous vehicles. Deep learning-based lane detection methods are leading development in this sector. This paper proposes an algorithm named Deep Learning-based Lane Detection (DLbLD), a Convolutional Neural Network (CNN)-based lane detection algorithm. The presented paradigm deploys CNN to detect line features in the image block, predict a point on the lane line part, and project all the detected points for each frame into one-dimensional form before applying K-mean clustering to assign points to related lane lines. Extensive tests on different benchmarks were done to evaluate the performance of the proposed algorithm. The results demonstrate that the introduced DLbLD scheme achieves state-of-the-art performance, where F1 scores of 97.19 and 79.02 have been recorded for TuSimple and CU-Lane benchmarks, respectively. Nevertheless, results indicate the high accuracy of the proposed algorithm.\",\"PeriodicalId\":46234,\"journal\":{\"name\":\"Pertanika Journal of Science and Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2024-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pertanika Journal of Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.47836/pjst.32.4.19\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pertanika Journal of Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47836/pjst.32.4.19","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

车道检测是自动驾驶汽车的一项基本任务。基于深度学习的车道检测方法引领着这一领域的发展。本文提出了一种名为基于深度学习的车道检测(DLbLD)的算法,这是一种基于卷积神经网络(CNN)的车道检测算法。所提出的范例利用卷积神经网络检测图像块中的线条特征,预测车道线部分上的一个点,并将每帧检测到的所有点投影成一维形式,然后应用 K-mean 聚类将点分配给相关的车道线。为了评估所提算法的性能,对不同的基准进行了广泛的测试。结果表明,引入的 DLbLD 方案达到了最先进的性能,在 TuSimple 和 CU-Lane 基准中分别获得了 97.19 和 79.02 的 F1 分数。尽管如此,结果表明所提出的算法具有很高的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Vision-Based Multi-Stages Lane Detection Algorithm
Lane detection is an essential task for autonomous vehicles. Deep learning-based lane detection methods are leading development in this sector. This paper proposes an algorithm named Deep Learning-based Lane Detection (DLbLD), a Convolutional Neural Network (CNN)-based lane detection algorithm. The presented paradigm deploys CNN to detect line features in the image block, predict a point on the lane line part, and project all the detected points for each frame into one-dimensional form before applying K-mean clustering to assign points to related lane lines. Extensive tests on different benchmarks were done to evaluate the performance of the proposed algorithm. The results demonstrate that the introduced DLbLD scheme achieves state-of-the-art performance, where F1 scores of 97.19 and 79.02 have been recorded for TuSimple and CU-Lane benchmarks, respectively. Nevertheless, results indicate the high accuracy of the proposed algorithm.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Pertanika Journal of Science and Technology
Pertanika Journal of Science and Technology MULTIDISCIPLINARY SCIENCES-
CiteScore
1.50
自引率
16.70%
发文量
178
期刊介绍: Pertanika Journal of Science and Technology aims to provide a forum for high quality research related to science and engineering research. Areas relevant to the scope of the journal include: bioinformatics, bioscience, biotechnology and bio-molecular sciences, chemistry, computer science, ecology, engineering, engineering design, environmental control and management, mathematics and statistics, medicine and health sciences, nanotechnology, physics, safety and emergency management, and related fields of study.
期刊最新文献
A Review on the Development of Microcarriers for Cell Culture Applications The Compatibility of Cement Bonded Fibreboard Through Dimensional Stability Analysis: A Review Bending Effects on Polyvinyl Alcohol Thin Film for Flexible Wearable Antenna Substrate Mesh Optimisation for General 3D Printed Objects with Cusp-Height Triangulation Approach The Riblet Short-Slot Coupler Using Substrate Integrated Waveguide (SIW) for High-frequency Applications
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1