Rain Rendering and Construction of Rain Vehicle Color-24 Dataset

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.

Automated summary

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.