A Real-Time Deraining Network with Rain Feature Perception for Autonomous Driving
Bibliographic record
Abstract
Autonomous driving systems heavily rely on clear visual input, but rainy weather significantly degrades image quality, posing challenges to reliable perception and decision-making. To address this issue, this paper proposes the Feature-Aware & Adaptive Image Rain Removal Network (FAIR-Net), a lightweight deraining network, which incorporates rain streak direction and raindrop saliency perception with adaptive feature fusion. The model explicitly captures rain-induced interference and dynamically adjusts feature integration to improve image restoration under complex rain conditions. Experiments on two representative datasets, Rain Cityscapes and Raindrop Cityscapes, show that FAIR-Net achieves superior performance in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) compared to existing methods, while maintaining high real-time efficiency. Specifically, FAIR-Net achieves 32.25 dB PSNR and 0.9815 SSIM on Rain Cityscapes, 35.40 dB PSNR and 0.9850 SSIM on Raindrop Cityscapes, with an inference speed of 101.9 FPS, making it highly suitable for deployment in autonomous driving scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".