Fog-Aware Adaptive YOLO for Object Detection in Adverse Weather
Bibliographic record
Abstract
Object detection in adverse weather conditions such as foggy environments is one of the main challenges in autonomous vehicles due to the significant reduction in visibility and performance of sensors. Although there are many publications to modify object detection in foggy environments, they are unable to manage both normal and foggy scenarios at the same time. In this paper, we propose a fog-aware adaptive YOLO algorithm for object detection in foggy environments. Our method first categorizes images into two groups based on their level of fogginess, normal and foggy, using a novel fog evaluator algorithm. In the next step, a standard YOLO algorithm is applied to normal images, while an image-adaptive YOLO algorithm is used for foggy images. Our approach provides a dynamic solution to evaluate the fog level of input images and adjust the detection algorithm accordingly, which can be applied in various realworld applications such as autonomous vehicles. Experimental results on the VOC dataset demonstrate the effectiveness of our approach in improving object detection performance in foggy conditions. The proposed method has a reasonable improvement in mean average precision compared to existing state-of-the-art methods in foggy weather conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".