Hierarchical-level rain image generative model based on GAN
Zhenyuan Liu Tong Jia Xingyu Xing Jianfeng Wu Junyi Chen
Autonomous vehicles are exposed to various weather during operation, which is
likely to trigger the performance limitations of the perception system, leading
to the safety of the intended functionality (SOTIF) problems. To efficiently
generate data for testing the performance of visual perception algorithms under
various weather conditions, a hierarchical-level rain image generative model,
rain conditional CycleGAN (RCCycleGAN), is constructed. RCCycleGAN is based on
the generative adversarial network (GAN) and can generate images of light,
medium, and heavy rain. Different rain intensities are introduced as labels in
conditional GAN (CGAN). Meanwhile, the model structure is optimized and the
training strategy is adjusted to alleviate the problem of mode collapse. In
addition, natural rain images of different intensities are collected and
processed for model training and validation. 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 to validate the effectiveness of the
model tuning.