Deep Learning Traffic Sign Recognition in Autonomous Vehicle

被引:0
|
作者
Alhabshee, Sharifah Maryam [1 ]
bin Shamsudin, Abu Ubaidah [2 ]
机构
[1] Univ Tun Hussein Onn Malaysia, Fac Elect & Elect Engn, Johor Baharu, Malaysia
[2] Univ Tun Hussein Onn Malaysia, Fac Elect & Elect Engn, MINT SRC, Johor Baharu, Malaysia
基金
奥地利科学基金会;
关键词
Deep learning; YOLOv3; Recognition; Traffic sign;
D O I
10.1109/scored50371.2020.9251034
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In this paper, a deep learning method is used to make a system for traffic sign recognition. You Only Look Once (YOLOv3) is used as it has a quick response in terms of real-time data reliability followed by high accuracy and robust performance. This study applies image preprocessing for better decision making for the recognition system in a different environment which includes lighting and weather. This is to ensure that the approach used is safe to be installed in autonomous vehicles. A comparison of images trained and tested will be demonstrated. The accuracy reach up to 100% and time to recognize traffic sign in image is in 36.907457 seconds. An analysis is done to ensure the error rate is reduced as training is done in a longer period.
引用
收藏
页码:438 / 442
页数:5
相关论文
共 50 条
  • [1] Development of Deep Learning Models for Traffic Sign Recognition in Autonomous Vehicles
    Kozhamkulova, Zhadra
    Bidakhmet, Zhanar
    Vorogushina, Marina
    Tashenova, Zhuldyz
    Tussupova, Bella
    Nurlybaeva, Elmira
    Kambarov, Dastan
    [J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2024, 15 (05) : 913 - 920
  • [2] Deep learning-based traffic sign recognition for unmanned autonomous vehicles
    Zang, Di
    Wei, Zhihua
    Bao, Maomao
    Cheng, Jiujun
    Zhang, Dongdong
    Tang, Keshuang
    Li, Xin
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING, 2018, 232 (05) : 497 - 505
  • [3] Traffic sign recognition based on deep learning
    Yanzhao Zhu
    Wei Qi Yan
    [J]. Multimedia Tools and Applications, 2022, 81 : 17779 - 17791
  • [4] Traffic sign recognition based on deep learning
    Zhu, Yanzhao
    Yan, Wei Qi
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2022, 81 (13) : 17779 - 17791
  • [5] Traffic sign recognition using deep learning
    Patel, Vraj
    Mehta, Joy
    Iyer, Saurab
    Sharma, Ankit K.
    [J]. International Journal of Vehicle Autonomous Systems, 2023, 16 (2-4) : 97 - 107
  • [6] Deep Transfer Learning for Traffic Sign Recognition
    Rosario, Grant
    Sonderman, Thomas
    Zhu, Xingquan
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION (IRI), 2018, : 178 - 185
  • [7] Improved traffic sign recognition system (itsrs) for autonomous vehicle based on deep convolutional neural network
    Kheder, Mohammed Qader
    Mohammed, Aree Ali
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 83 (22) : 61821 - 61841
  • [8] Integrated Framework of Autonomous Vehicle with Traffic Sign Recognition in Simulation Environment
    Prabhu, Nikhil
    Min, Sewoong
    Nam, Haewoon
    Tewolde, Girma
    Kwon, Jaerock
    [J]. 2020 IEEE INTERNATIONAL CONFERENCE ON ELECTRO INFORMATION TECHNOLOGY (EIT), 2020, : 514 - 521
  • [9] Modelling and simulation of an autonomous vehicle based on Alexnet for traffic sign recognition
    Liu, Chao
    [J]. International Journal of Vehicle Systems Modelling and Testing, 2024, 18 (01) : 62 - 77
  • [10] Traffic Sign Detection and Recognition Based on Deep Learning
    Zhang, H.
    Zhao, J.
    [J]. ENGINEERING LETTERS, 2022, 30 (02) : 666 - 673