Hierarchical-level rain image generative model based on GAN
This work addresses safety concerns in autonomous vehicles by providing a method to generate data for testing visual perception algorithms under various rain conditions, though it is incremental as it builds on existing GAN frameworks.
The paper tackles the problem of generating realistic rain images for testing autonomous vehicle perception systems by proposing a hierarchical-level rain image generative model called RCCycleGAN, which improves PSNR by up to 2.58 dB and SSIM by up to 18% compared to baseline models.
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.