Chinese Journal of Liquid Crystals and Displays, Volume. 37, Issue 9, 1228(2022)

Real-time detection model of highway vehicle based on YOLOv5s

Yuan-feng LIU1, Hai-jun JI2, and Li-bo LIU1、*
Author Affiliations
  • 1School of Information Engineering, Ningxia University, Yinchuan 750021, China
  • 2Ningxia Road Network Monitoring and Emergency Response Center, Yinchuan 750021, China
  • show less
    References(26)

    [5] REDMON J, FARHADI A. YOLOv3: an incremental improvement[J]. arXiv, 1804. 02767(2018).

    [6] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[J]. arXiv preprint arXiv, 2020.

    [7] WANG Y X, SONG H S, LIANG H X et al. Highway vehicle object detection based on improved YOLOv4 method[J]. Computer Engineering and Applications, 57, 218-226(2021).

    [17] JETLEY S, LORD N A, LEE N et al. Learn to pay attention[C](2018).

    [23] ZHANG B L, QIN H R, JIANG S et al. A method of vehicle detection at night based on RetinaNet and optimized loss functions[J]. Automotive Engineering, 43, 1195-1202(2021).

    [26] SHI J, CHENG Q, JIN L S et al. Fine-grained vehicle detection and classification model for video structuring description[J]. Automotive Engineering, 43, 1427-1434(2021).

    Tools

    Get Citation

    Copy Citation Text

    Yuan-feng LIU, Hai-jun JI, Li-bo LIU. Real-time detection model of highway vehicle based on YOLOv5s[J]. Chinese Journal of Liquid Crystals and Displays, 2022, 37(9): 1228

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Research Articles

    Received: Jan. 24, 2022

    Accepted: --

    Published Online: Sep. 19, 2022

    The Author Email: Li-bo LIU (liulib@163.com)

    DOI:10.37188/CJLCD.2022-0026

    Topics