A Method for Traffic Sign Detection and Recognition Based on Genetic Algorithm

被引:0
|
作者
Kobayashi, Mitsuhiro [1 ]
Baba, Mitsuru [2 ]
Ohtani, Kozo [4 ]
Li, Li [3 ]
机构
[1] Ibaraki Univ, Grad Sch Sci & Engn, Ibaraki, Japan
[2] Ibaraki Univ, Coll Engn, Ibaraki, Japan
[3] Wuhan Text Univ, Dept Math & Comp, Wuhan, Peoples R China
[4] Hiroshima Inst Technol, Fac Appl Informat Sci, Hiroshima, Japan
关键词
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents a method for detection and recognition of traffic signs. We proposed a new recognition approach of traffic signs, which has the feature of introducing Genetic Algorithm (GA). We designed and built a prototype system by implementation of C++ and OpenCV library. The experimental results show that our approach could have good prospects for automatic detection and recognition of traffic signs.
引用
收藏
页码:455 / 460
页数:6
相关论文
共 50 条
  • [21] The automatic detection and recognition of the Traffic Sign
    Gao, Shangbing
    Zhang, Yan
    2016 INTERNATIONAL CONFERENCE ON VIRTUAL REALITY AND VISUALIZATION (ICVRV 2016), 2016, : 52 - 56
  • [22] Traffic Sign Detection and Recognition Using YOLO Object Detection Algorithm: A Systematic Review
    Flores-Calero, Marco
    Astudillo, Cesar A.
    Guevara, Diego
    Maza, Jessica
    Lita, Bryan S.
    Defaz, Bryan
    Ante, Juan S.
    Zabala-Blanco, David
    Armingol Moreno, Jose Maria
    MATHEMATICS, 2024, 12 (02)
  • [23] Traffic sign detection and recognition based on pyramidal convolutional networks
    Zhenwen Liang
    Jie Shao
    Dongyang Zhang
    Lianli Gao
    Neural Computing and Applications, 2020, 32 : 6533 - 6543
  • [24] Traffic sign detection and recognition based on pyramidal convolutional networks
    Liang, Zhenwen
    Shao, Jie
    Zhang, Dongyang
    Gao, Lianli
    NEURAL COMPUTING & APPLICATIONS, 2020, 32 (11): : 6533 - 6543
  • [25] Traffic Sign Detection and Recognition Based on Convolutional Neural Network
    Sun, Ying
    Ge, Pingshu
    Liu, Dequan
    2019 CHINESE AUTOMATION CONGRESS (CAC2019), 2019, : 2851 - 2854
  • [26] Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling
    Christine Dewi
    Rung-Ching Chen
    Hui Yu
    Xiaoyi Jiang
    Journal of Ambient Intelligence and Humanized Computing, 2023, 14 : 8135 - 8152
  • [27] Traffic Sign Detection and Recognition using Neural Networks and Histogram based Selection of Segmentation Method
    Fistrek, Tomislav
    Loncaric, Sven
    53RD INTERNATIONAL SYMPOSIUM ELMAR-2011, 2011, : 51 - 54
  • [28] Research on Traffic Sign Recognition Method Based on Transfer Learning
    Gao, Decheng
    Jiang, Weidong
    He, Yinxia
    Min, Jie
    PROCEEDINGS OF 2021 2ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INFORMATION SYSTEMS (ICAIIS '21), 2021,
  • [29] A Traffic Sign Recognition Method Based on Deep Visual Feature
    Lin, Feng
    Lai, Yan
    Lin, Lan
    Yuan, Yuxin
    2016 PROGRESS IN ELECTROMAGNETICS RESEARCH SYMPOSIUM (PIERS), 2016, : 2247 - 2250
  • [30] Traffic Sign Recognition Method Based on Deep Residual Network
    Zhang, Jiada
    Xu, Xuebin
    Hou, Xinglin
    Gu, Zhuangzhuang
    Zhao, Yuqing
    Liu, Yuhao
    Zhang, Guohua
    BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 2020, 127 : 103 - 103