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
  • [21] A Study on Different Types of Convolutions in Deep Learning in the Area of Lane Detection
    Rajalakshmi, T. S.
    Senthilnathan, R.
    DISTRIBUTED COMPUTING AND OPTIMIZATION TECHNIQUES, ICDCOT 2021, 2022, 903 : 79 - 88
  • [22] RoadWay lane detection for autonomous driving vehicles via deep learning
    Singal, Gaurav
    Singhal, Himanshu
    Kushwaha, Riti
    Veeramsetty, Venkataramana
    Badal, Tapas
    Lamba, Sonu
    MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (04) : 4965 - 4978
  • [23] A hybrid spatial-temporal deep learning architecture for lane detection
    Dong, Yongqi
    Patil, Sandeep
    van Arem, Bart
    Farah, Haneen
    COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 2023, 38 (01) : 67 - 86
  • [24] Spatial-Temproal Based Lane Detection Using Deep Learning
    Huang, Yuhao
    Chen, Shitao
    Chen, Yu
    Jian, Zhiqiang
    Zheng, Nanning
    ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2018, 2018, 519 : 143 - 154
  • [25] Review on generative deep learning models and datasets for intrusion detection systems
    Ketepall G.
    Bulla P.
    1600, International Information and Engineering Technology Association (34): : 215 - 226
  • [26] Deep learning methods for Fake News detection
    Kresnakova, Viera Maslej
    Sarnovsky, Martin
    Butka, Peter
    IEEE JOINT 19TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND INFORMATICS AND 7TH INTERNATIONAL CONFERENCE ON RECENT ACHIEVEMENTS IN MECHATRONICS, AUTOMATION, COMPUTER SCIENCES AND ROBOTICS (CINTI-MACRO 2019), 2019, : 143 - 148
  • [27] Detection of Malicious Binaries by Deep Learning Methods
    Chukka, Anantha Rao
    Devi, V. Susheela
    PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON INTERNET OF THINGS, BIG DATA AND SECURITY (IOTBDS), 2021, : 132 - 139
  • [28] Robust Deep Learning Methods for Anomaly Detection
    Chalapathy, Raghavendra
    Khoa, Nguyen Lu Dang
    Chawla, Sanjay
    KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, 2020, : 3507 - 3508
  • [29] Deep Learning Methods for Tree Detection and Classification
    Zhang, Yang
    Wang, Yizhen
    Tang, Zhicheng
    Zhai, Zhenduo
    Shang, Yi
    Viegut, Reid
    2022 IEEE 4TH INTERNATIONAL CONFERENCE ON COGNITIVE MACHINE INTELLIGENCE, COGMI, 2022, : 148 - 155
  • [30] Deep Learning-Based Dermatological Condition Detection: A Systematic Review With Recent Methods, Datasets, Challenges, and Future Directions
    Noronha, Stephanie S.
    Mehta, Mayuri A.
    Garg, Dweepna
    Kotecha, Ketan
    Abraham, Ajith
    IEEE ACCESS, 2023, 11 : 140348 - 140381
12345