Visual perception systems for autonomous vehicles are exposed to a wide variety
of complex weather conditions, among which rainfall is one of the weather
conditions with high exposure. Therefore, it is necessary to construct a model
that can efficiently generate a large number of images with different rainfall
intensities to help test the visual perception system under rainfall conditions.
However, the existing datasets either do not contain multilevel rainfall or are
synthetic images. It is difficult to support the construction of the model. In
this paper, the natural rainfall images of different rainfall intensities were
first collected and produced a natural multilevel rain dataset. The dataset
includes no rain and three levels (light, medium and heavy) of rainfall with the
number of 629, 210, 248 and 193 respectively, totaling 1280 images. The dataset
is open source and available online via:
https://github.com/raydison/natural-multilevel-rain-dataset-NMRD.
Subsequently, a hierarchical-level rain image generative model, rain conditional
CycleGAN (RCCycleGAN), is constructed. RCCycleGAN is based on the generative
adversarial network (GAN), which can generate images of light, medium and heavy
rain by inputting no rain images into the model. In the process of model tuning,
a total of three modifications are made based on the DerainCycleGAN, including
introducing different rainfall intensity labels, replacing the activation
function, and adjusting the training strategy. Compared with the two baseline
models, CycleGAN and DerainCycleGAN, the peak signal-to-noise ratio (PSNR) of
RCCycleGAN on the test dataset is improved by 2.58 dB and 0.74 dB, and the
structural similarity (SSIM) is improved by 18% and 8%, respectively. The
ablation experiments are also carried out and validate the effectiveness of the
model tuning.