Journal
MATHEMATICS
Volume 10, Issue 17, Pages -Publisher
MDPI
DOI: 10.3390/math10173210
Keywords
rain rendering; deep convolutional neural network; rain datasets; identification of vehicle color; single-image deraining algorithm
Categories
Funding
- National Natural Science Foundation of China [62071378]
- Shaanxi Province International Science and Technology Cooperation Program [2022KW-04]
- Xi'an Science and Technology Plan Project [21XJZZ0072]
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This paper presents a method to improve vehicle color recognition algorithms by rendering rain images and constructs a new Rain Vehicle Color-24 dataset. Experimental results show that the algorithms trained on this dataset achieve recognition accuracy of around 72% and 90% on rainy and sunny days, respectively.
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 Rain Vehicle Color-24 dataset by rain-image rendering using PS technology and a SyRaGAN algorithm based on the Vehicle Color-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 Rain Vehicle Color-24 improve accuracy to around 72% and 90% on rainy and sunny days, respectively. The code is available at humingdi2005@github.com.
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