Ultra-Fast Deraining Plugin for Vision-Based Perception of Autonomous Driving

Rain deviates the distribution of rainy images and the clean, rain-free data typically used during perception model training, this kind of out-of-distribution (OOD) issue making it difficult for models to generalize effectively in rainy scenarios, leading the performance degrade of autonomous perception systems in visual tasks such as lane detection and depth estimation, posing serious safety risks. To address this issue, we propose the Ultra-Fast Deraining Plugin (UFDP), a model-efficient deraining solution specifically designed to realign the distribution of rainy images and their rain-free counterparts. UFDP not only effectively removes rain from images but also seamlessly integrates into existing visual perception models, significantly enhancing their robustness and stability under rainy conditions. Through a detailed analysis of single-image color histograms and dataset-level distribution, we demonstrate how UFDP improves the similarity between rainy and non-rainy image distributions. Additionally, qualitative and quantitative results highlight UFDP’s superiority over state-of-the-art (SOTA) methods, showing a 5.4% improvement in SSIM and 8.1% in PSNR. UFDP also excels in terms of efficiency, achieving 7 times higher FPS than the slowest method, reducing FLOPs by 53.7 times, and using 28.8 times fewer MACs, with 6.2 times fewer parameters. This makes UFDP an ideal solution for ensuring reliable performance in autonomous driving visual perception systems, particularly in challenging rainy environments.