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Rain Rendering and Construction of Rain Vehicle Color -24 Dataset

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Listed:
  • Mingdi Hu

    (School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Chang’an West St., Chang’an District, Xi’an 710121, China)

  • Chenrui Wang

    (School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Chang’an West St., Chang’an District, Xi’an 710121, China)

  • Jingbing Yang

    (School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Chang’an West St., Chang’an District, Xi’an 710121, China)

  • Yi Wu

    (School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Chang’an West St., Chang’an District, Xi’an 710121, China)

  • Jiulun Fan

    (School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Chang’an West St., Chang’an District, Xi’an 710121, China)

  • Bingyi Jing

    (Department of Statistics & Data Science, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, China)

Abstract

The fine identification of vehicle color can assist in criminal investigation or intelligent traffic management law enforcement. Since almost all vehicle-color datasets that are used to train models are collected in good weather, the existing vehicle-color recognition algorithms typically show poor performance for outdoor visual tasks. In this paper we construct a new R a i n V e h i c l e C o l o r -24 dataset by rain-image rendering using P S technology and a S y R a G A N algorithm based on the V e h i c l e C o l o r -24 dataset. The dataset contains a total of 40,300 rain images with 125 different rain patterns, which can be used to train deep neural networks for specific vehicle-color recognition tasks. Experiments show that the vehicle-color recognition algorithms trained on the new dataset R a i n V e h i c l e C o l o r -24 improve accuracy to around 72 % and 90 % on rainy and sunny days, respectively. The code is available at humingdi2005@github.com.

Suggested Citation

  • Mingdi Hu & Chenrui Wang & Jingbing Yang & Yi Wu & Jiulun Fan & Bingyi Jing, 2022. "Rain Rendering and Construction of Rain Vehicle Color -24 Dataset," Mathematics, MDPI, vol. 10(17), pages 1-18, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3210-:d:907258
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    References listed on IDEAS

    as
    1. Nan Sheng & Xiaohong Zhang, 2022. "Regular Partial Residuated Lattices and Their Filters," Mathematics, MDPI, vol. 10(14), pages 1-16, July.
    2. Ping Xue & Hai He, 2021. "Research of Single Image Rain Removal Algorithm Based on LBP-CGAN Rain Generation Method," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, July.
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    Cited by:

    1. Xiyang Yang & Shiqing Zhang & Xinjun Zhang & Fusheng Yu, 2022. "Polynomial Fuzzy Information Granule-Based Time Series Prediction," Mathematics, MDPI, vol. 10(23), pages 1-21, November.
    2. Mingdi Hu & Yi Wu & Jiulun Fan & Bingyi Jing, 2022. "Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions," Mathematics, MDPI, vol. 10(19), pages 1-16, September.

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