Rain Rendering and Construction of Rain Vehicle Color -24 Dataset

Mingdi Hu (), Chenrui Wang, Jingbing Yang, Yi Wu, Jiulun Fan and Bingyi Jing ()
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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

Mathematics, 2022, vol. 10, issue 17, 1-18

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.

Keywords: rain rendering; deep convolutional neural network; rain datasets; identification of vehicle color; single-image deraining algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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