统一处理恶劣天气下的目标检测，一次网络完成识别与图像修复。
UniDet-D: A Unified Dynamic Spectral Attention Model for Object Detection under Adverse Weathers
- 动态光谱注意力机制，自适应增强关键视觉频段。
- 在雨、雾、雪等多类天气下检测精度显著提升。
- 可泛化至沙尘暴等未见天气，适合真实场景部署。
现实世界中的目标检测面临复杂退化问题，如雨、雾、雪、低光照等恶劣天气导致图像质量下降。现有方法大多针对单一天气类型，泛化能力差且未能充分利用视觉特征。基于对恶劣天气下视觉细节丢失机制的理论分析，本文提出UniDet-D统一框架，实现单一网络内同时完成目标检测与图像恢复。其核心是动态光谱注意力机制，可自适应强调有效频段并抑制无关成分，从而在多种退化类型下获得更鲁棒、更具判别性的特征表示。大量实验表明，UniDet-D在各类恶劣天气条件下均取得优异检测性能，并在沙尘暴、雨雾混合等未见过的天气场景中展现良好泛化能力，具备实际应用潜力。
原文摘要 · Abstract (English)
Real-world object detection is a challenging task where the captured images/videos often suffer from complex degradations due to various adverse weather conditions such as rain, fog, snow, low-light, etc. Despite extensive prior efforts, most existing methods are designed for one specific type of adverse weather with constraints of poor generalization, under-utilization of visual features while handling various image degradations. Leveraging a theoretical analysis on how critical visual details are lost in adverse-weather images, we design UniDet-D, a unified framework that tackles the challenge of object detection under various adverse weather conditions, and achieves object detection and image restoration within a single network. Specifically, the proposed UniDet-D incorporates a dynamic spectral attention mechanism that adaptively emphasizes informative spectral components while suppressing irrelevant ones, enabling more robust and discriminative feature representation across various degradation types. Extensive experiments show that UniDet-D achieves superior detection accuracy across different types of adverse-weather degradation. Furthermore, UniDet-D demonstrates superior generalization towards unseen adverse weather conditions such as sandstorms and rain-fog mixtures, highlighting its great potential for real-world deployment.
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