Lane Detection with Deep Learning: Methods and Datasets

被引:0
|
作者
Li, Junyan [1 ]
机构
[1] Shandong Univ, Sch Mech Elect & Informat Engn, Weihai 264209, Peoples R China
来源
INFORMATION TECHNOLOGY AND CONTROL | 2023年 / 52卷 / 02期
关键词
Lane Detection; Deep Learning; Convolutional Neural Network; Dataset;
D O I
10.5755/j01.itc.52.2.32841
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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, a comprehensive literature review for lane detection, especially those with deep learning models, is presented. Furthermore, the latest collection of lane detection datasets is presented. The research gap is further filled by proposing a novel lane detection dataset named MudLane, which focuses on the lane detection task on suburban roads.
引用
收藏
页码:297 / 308
页数:12
相关论文
共 50 条
  • [41] Evaluation of unsupervised optical flow methods for deep learning in real world datasets
    Marez, Diego
    Harguess, Josh
    GEOSPATIAL INFORMATICS IX, 2019, 10992
  • [42] Deep-learning-based counting methods, datasets, and applications in agriculture: a review
    Farjon, Guy
    Huijun, Liu
    Edan, Yael
    PRECISION AGRICULTURE, 2023, 24 (05) : 1683 - 1711
  • [43] Deep-learning-based counting methods, datasets, and applications in agriculture: a review
    Guy Farjon
    Liu Huijun
    Yael Edan
    Precision Agriculture, 2023, 24 : 1683 - 1711
  • [44] Forensic detection of heterogeneous activity in data using deep learning methods
    Nyarko, Benedicta Nana Esi
    Bin, Wu
    Zhou, Jinzhi
    Odoom, Justice
    Danso, Samuel Akwasi
    Addai, Gyarteng Emmanuel Sarpong
    INTELLIGENT SYSTEMS WITH APPLICATIONS, 2024, 21
  • [45] Bridging the Gap of Lane Detection Performance Between Different Datasets: Unified Viewpoint Transformation
    Wen, Tuopu
    Yang, Diange
    Jiang, Kun
    Yu, Chunlei
    Lin, Jiaxin
    Wijaya, Benny
    Jiao, Xinyu
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 22 (10) : 6198 - 6207
  • [46] CenFind: a deep-learning pipeline for efficient centriole detection in microscopy datasets
    Burgy, Leo
    Weigert, Martin
    Hatzopoulos, Georgios
    Minder, Matthias
    Journe, Adrien
    Rahi, Sahand Jamal
    Gonczy, Pierre
    BMC BIOINFORMATICS, 2023, 24 (01)
  • [47] Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study
    Ferrag, Mohamed Amine
    Maglaras, Leandros
    Moschoyiannis, Sotiris
    Janicke, Helge
    JOURNAL OF INFORMATION SECURITY AND APPLICATIONS, 2020, 50
  • [48] Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic Datasets
    Chakraborty, Partha
    Arumugam, Krishna Kanth
    Alfadel, Mahmoud
    Nagappan, Meiyappan
    McIntosh, Shane
    IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2024, 50 (08) : 2163 - 2177
  • [49] Deep Learning for Audio Event Detection and Tagging on Low-Resource Datasets
    Morfi, Veronica
    Stowell, Dan
    APPLIED SCIENCES-BASEL, 2018, 8 (08):
  • [50] CenFind: a deep-learning pipeline for efficient centriole detection in microscopy datasets
    Léo Bürgy
    Martin Weigert
    Georgios Hatzopoulos
    Matthias Minder
    Adrien Journé
    Sahand Jamal Rahi
    Pierre Gönczy
    BMC Bioinformatics, 24