Real-Time Traffic Sign Detection and Recognition System Based on FriendlyARM Tiny4412 Board

被引:0
|
作者
Truong Quang Vinh [1 ]
机构
[1] Ho Chi Minh City Univ Technol HCM VNU, Fac Elect & Elect Engn, Ho Chi Minh, Vietnam
关键词
traffic sign; color segmentation; FriendlyARM; ARM Cortex-A9; SVM;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper presents a design and implementation of the real-time traffic sign detection and recognition system based FriendlyARM Tiny4412 board. We develop an algorithm for detecting and recognizing the traffic signs in Vietnam with real-time processing capability and high accuracy. To achieve these objectives, we employ three main techniques consisting of traffic sign extraction based on chromatic color segmentation, shape matching, and support vector machine (SVM). Moreover, we apply multi-threading method for quad-core ARM Cortex-A9 processor on FriendlyARM board to enhance the real-time capability of the system. The experimental result shows that our system can detect and recognize the traffic signs with accuracy of 90.1% at 15 frames per second on FriendlyARM Tiny4412 board. The proposed system can be equipped on cars to support drivers tracking traffic signs.
引用
收藏
页码:142 / 146
页数:5
相关论文
共 50 条
  • [41] Smartphone Based Mass Traffic Sign Recognition for Real-time Navigation Maps Enhancement
    Trasnea, Bogdan
    Macesanu, Gigel
    Grigorescu, Sorin
    Cocias, Tiberiu-Teodor
    2017 INTERNATIONAL CONFERENCE ON OPTIMIZATION OF ELECTRICAL AND ELECTRONIC EQUIPMENT (OPTIM) & 2017 INTL AEGEAN CONFERENCE ON ELECTRICAL MACHINES AND POWER ELECTRONICS (ACEMP), 2017, : 1138 - 1144
  • [42] Real-Time Traffic Sign Recognition using YOLOv3 based Detector
    Rajendran, Shehan P.
    Shine, Linu
    Pradeep, R.
    Vijayaraghavan, Sajith
    2019 10TH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND NETWORKING TECHNOLOGIES (ICCCNT), 2019,
  • [43] Real-Time Traffic Sign Detection and Classification Using Machine Learning and Optical Character Recognition
    Ciuntu, Victor
    Ferdowsi, Hasan
    2020 IEEE INTERNATIONAL CONFERENCE ON ELECTRO INFORMATION TECHNOLOGY (EIT), 2020, : 480 - 486
  • [44] DriastSystem: A Computer Vision Based Device for Real Time Traffic Sign Detection and Recognition
    Tekieli, Marcin
    Slonski, Marek
    ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, PT I, 2012, 7267 : 608 - 616
  • [45] Real-Time Traffic Sign Detection using Capsule Network
    Pari, Neelavathy S.
    Mohana, T.
    Akshaya, V
    2019 11TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING (ICOAC 2019), 2019, : 193 - 196
  • [46] Real-Time Detection and Recognition of Road Traffic Signs
    Greenhalgh, Jack
    Mirmehdi, Majid
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2012, 13 (04) : 1498 - 1506
  • [47] RIECNN: real-time image enhanced CNN for traffic sign recognition
    Reem Abdel-Salam
    Rana Mostafa
    Ahmed H. Abdel-Gawad
    Neural Computing and Applications, 2022, 34 : 6085 - 6096
  • [48] Real-Time Traffic-Sign Recognition Using Tree Classifiers
    Zaklouta, Fatin
    Stanciulescu, Bogdan
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2012, 13 (04) : 1507 - 1514
  • [49] RESOURCE EFFICIENT HARDWARE IMPLEMENTATION FOR REAL-TIME TRAFFIC SIGN RECOGNITION
    Weng, Huai-Mao
    Chiu, Ching-Te
    2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2018, : 1120 - 1124
  • [50] Real-Time Traffic Sign Recognition using Color Segmentation and SVM
    Ardianto, Sandy
    Chen, Chih-Jung
    Hang, Hsueh-Ming
    2017 INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING (IWSSIP), 2017,