用图像转换技术提升夜间天气识别准确率，让交通摄像头全天候可用。
ClearVision: Leveraging CycleGAN and SigLIP-2 for Robust All-Weather Classification in Traffic Camera Imagery
- 用CycleGAN把夜景转成日间风格，保留天气信息同时改善夜间识别
- 夜间识别准确率从63.40%提升至85.90%，整体准确率达94.00%
- 模型训练快89%，推理快80%，适合部署在现有交通监控系统
恶劣天气威胁交通安全，亟需从交通摄像头影像中实现鲁棒的实时天气检测。本文提出一种新框架，结合基于CycleGAN的域适应与高效对比学习，提升夜间低光条件下的天气分类性能。利用轻量级SigLIP-2模型（采用成对sigmoid损失降低计算开销），并融合CycleGAN将夜间图像转换为类日间表征，同时保留天气特征。在爱荷华州交通部数据集上，基线EVA-02+CLIP模型在三种天气（无降水、雨、雪）下每类准确率为96.55%，昼夜总体准确率96.55%，但存在显著昼夜差距（白天97.21%，夜间63.40%）。引入CycleGAN后，EVA-02提升至每类准确率97.01%，昼夜总体准确率96.85%，夜间性能提升至82.45%。最优配置Vision-SigLIP-2 + Text-SigLIP-2 + CycleGAN + Contrastive在夜间达到最高准确率85.90%，每类准确率94.00%，昼夜总体准确率93.35%。相比EVA-02，该模型训练时间减少89%（6小时→40分钟），推理时间减少80%（15秒→3秒）。昼夜性能差距由33.81个百分点缩小至8.90个百分点，为利用现有摄像头基础设施提供了一种可扩展、高效的全天气分类方案。
原文摘要 · Abstract (English)
Adverse weather conditions challenge safe transportation, necessitating robust real-time weather detection from traffic camera imagery. We propose a novel framework combining CycleGAN-based domain adaptation with efficient contrastive learning to enhance weather classification, particularly in low-light nighttime conditions. Our approach leverages the lightweight SigLIP-2 model, which employs pairwise sigmoid loss to reduce computational demands, integrated with CycleGAN to transform nighttime images into day-like representations while preserving weather cues. Evaluated on an Iowa Department of Transportation dataset, the baseline EVA-02 model with CLIP achieves a per-class overall accuracy of 96.55\% across three weather conditions (No Precipitation, Rain, Snow) and a day/night overall accuracy of 96.55\%, but shows a significant day-night gap (97.21\% day vs.\ 63.40\% night). With CycleGAN, EVA-02 improves to 97.01\% per-class accuracy and 96.85\% day/night accuracy, boosting nighttime performance to 82.45\%. Our Vision-SigLIP-2 + Text-SigLIP-2 + CycleGAN + Contrastive configuration excels in nighttime scenarios, achieving the highest nighttime accuracy of 85.90\%, with 94.00\% per-class accuracy and 93.35\% day/night accuracy. This model reduces training time by 89\% (from 6 hours to 40 minutes) and inference time by 80\% (from 15 seconds to 3 seconds) compared to EVA-02. By narrowing the day-night performance gap from 33.81 to 8.90 percentage points, our framework provides a scalable, efficient solution for all-weather classification using existing camera infrastructure.
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