Improving object detection in challenging weather for autonomous driving via adversarial image translation
Bibliographic record
Abstract
Vision-based environmental perception is fundamental to autonomous driving, as it enables reliable detection and recognition of diverse objects in complex traffic environments. However, adverse weather conditions (such as rain, fog, and low-light conditions) significantly degrade image quality, thereby undermining the reliability of object detection algorithms. To address this challenge, we propose a two-stage framework designed to enhance object detection under adverse conditions. In the first stage, we design a lightweight Pix2Pix-based generative adversarial network (LP-GAN) that translates adverse-weather images into clear-weather counterparts, thereby alleviating visual degradation. In the second stage, the translated images are processed by a state-of-the-art object detector (YOLOv8) to enhance robustness and accuracy. Extensive experiments on the CARLA simulator demonstrate that the proposed framework substantially improves detection performance across diverse adverse conditions. Furthermore, the generated clear-weather images provide faithful and interpretable visual representations, which can facilitate human understanding and decision-making in autonomous driving. Overall, the proposed framework offers a practical and effective solution for weather-robust object detection, thereby contributing to safer and more reliable autonomous driving.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".