基于深度学习的车道检测:方法和数据集

IF 2 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Information Technology and Control Pub Date : 2023-07-15 DOI:10.5755/j01.itc.52.2.32841
Junyan Li
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引用次数: 0

摘要

车道检测问题一直被认为是自动驾驶计算机视觉中的一项重要任务。虽然它在文献中得到了大量的研究关注,但这个问题尚未完全解决。在本文中,我们提出了一个全面的文献综述车道检测,特别是那些与深度学习模型。介绍了最新的车道检测数据集。我们进一步提出了一种新的车道检测数据集MudLane,该数据集专注于郊区道路的车道检测任务,填补了研究空白。
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Lane Detection with Deep Learning: Methods and Datasets
Lane detection problem has been considered as an important computer vision task in autonomous driving. While it has received massive research attention in the literature, the problem is not yet fully solved. In this paper, we present a comprehensive literature review for lane detection, especially those with deep learning models. The latest collection of lane detection datasets is presented. We further fill the research gap by proposing a novel lane detection dataset named MudLane, which focuses on the lane detection task on suburban roads.
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来源期刊
Information Technology and Control
Information Technology and Control 工程技术-计算机:人工智能
CiteScore
2.70
自引率
9.10%
发文量
36
审稿时长
12 months
期刊介绍: Periodical journal covers a wide field of computer science and control systems related problems including: -Software and hardware engineering; -Management systems engineering; -Information systems and databases; -Embedded systems; -Physical systems modelling and application; -Computer networks and cloud computing; -Data visualization; -Human-computer interface; -Computer graphics, visual analytics, and multimedia systems.
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