Deep Learning Based Lane Line Detection and Segmentation Using Slice Image Feature

Jing. Guo, Herleeyandi Markoni
{"title":"Deep Learning Based Lane Line Detection and Segmentation Using Slice Image Feature","authors":"Jing. Guo, Herleeyandi Markoni","doi":"10.1109/ISPACS51563.2021.9651012","DOIUrl":null,"url":null,"abstract":"Nowadays, an effective driving assist system is expected to perform fast to observe and taking immediate decision. In particular for the detection of lane lines the system should able to perform faster and accurately locate the position of the lane lines. The majority of the existing work in this task relies on the frame-based processing in which the whole image is used as a feature. In addition for the case of high-resolution images the computational time is very significant and not feasible for practical applications in particular for embedded system. To overcome this problems, in this work a novel lane line detection system is proposed. The proposed approach utilizes the slice of a frame image as a feature and applies deep learning to detect and segment the lane line. Experimental results show that the proposed system can efficiently handle lane line detection by 60 times faster than the former schemes with superior accuracy.","PeriodicalId":359822,"journal":{"name":"2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPACS51563.2021.9651012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Nowadays, an effective driving assist system is expected to perform fast to observe and taking immediate decision. In particular for the detection of lane lines the system should able to perform faster and accurately locate the position of the lane lines. The majority of the existing work in this task relies on the frame-based processing in which the whole image is used as a feature. In addition for the case of high-resolution images the computational time is very significant and not feasible for practical applications in particular for embedded system. To overcome this problems, in this work a novel lane line detection system is proposed. The proposed approach utilizes the slice of a frame image as a feature and applies deep learning to detect and segment the lane line. Experimental results show that the proposed system can efficiently handle lane line detection by 60 times faster than the former schemes with superior accuracy.
基于切片图像特征的深度学习车道线检测与分割
如今,人们期望一种有效的驾驶辅助系统能够快速观察并立即做出决定。特别是对于车道线的检测,系统应该能够执行更快,准确地定位车道线的位置。该任务中的大部分现有工作依赖于基于帧的处理,其中将整个图像用作特征。此外,对于高分辨率图像,计算时间非常重要,不适合实际应用,特别是嵌入式系统。为了克服这一问题,本文提出了一种新的车道线检测系统。该方法利用帧图像的切片作为特征,并应用深度学习来检测和分割车道线。实验结果表明,该方法能有效地处理车道线检测问题,检测速度是现有方法的60倍,且检测精度较高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信