提升检测模型在雾天下的鲁棒性，通过感知损失、天气自适应注意力等方法增强
Weather-Aware Object Detection Transformer for Domain Adaptation
- 用感知损失实现跨域特征对齐，提取不变特征
- 引入雾感缩放机制，让注意力关注雾中关键区域
- 双流编码器融合清晰与雾天图像特征，提升泛化能力
RT-DETR 在多种计算机视觉任务中表现优异，但在雾天等恶劣天气下性能下降。本文提出三种新方法增强 RT-DETR 在雾天环境中的鲁棒性：(1) 基于感知损失的领域自适应，通过教师网络向学生网络传递域不变特征；(2) 天气自适应注意力，引入辅助雾天图像流，以雾感缩放增强注意力机制；(3) 天气融合编码器，采用双流架构，通过多头自注意力与交叉注意力融合清晰与雾天图像特征。尽管有结构创新，但所提方法均未持续优于基线 RT-DETR。我们分析其局限性及潜在原因，为未来天气感知目标检测研究提供洞见。
原文摘要 · Abstract (English)
RT-DETRs have shown strong performance across various computer vision tasks but are known to degrade under challenging weather conditions such as fog. In this work, we investigate three novel approaches to enhance RT-DETR robustness in foggy environments: (1) Domain Adaptation via Perceptual Loss, which distills domain-invariant features from a teacher network to a student using perceptual supervision; (2) Weather Adaptive Attention, which augments the attention mechanism with fog-sensitive scaling by introducing an auxiliary foggy image stream; and (3) Weather Fusion Encoder, which integrates a dual-stream encoder architecture that fuses clear and foggy image features via multi-head self and cross-attention. Despite the architectural innovations, none of the proposed methods consistently outperform the baseline RT-DETR. We analyze the limitations and potential causes, offering insights for future research in weather-aware object detection.
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