发表机构
University of Amsterdam; SUNY Empire State University（阿姆斯特丹大学; 纽约州立大学帝国州立学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究首次提出严重程度受控的雾基准，评估反无人机检测在雾下的鲁棒性，发现性能急剧非线性下降，雾感知训练可显著提升雾天检测但牺牲少量晴空精度，且简单晴雨对比低估了操作风险。
AI 中文摘要
基于视觉的反无人机系统必须在低能见度条件下工作，然而大多数基准仅使用晴空镜头，且以往的鲁棒性研究将恶劣天气视为简单的存在/缺失条件。因此，雾的严重程度对地对空无人机检测的影响仍知之甚少。本工作首次为此任务提出了严重程度受控的雾基准：将十个严重度级别的合成雾应用于Anti-UAV300数据集的RGB模态，比较了在晴空下训练的YOLOv5m基线与在晴空和雾天图像上训练的雾感知模型。检测性能急剧且非线性下降：退化集中在轻至中度雾中（beta约0.05-0.10），从晴空到最浓雾时mAP@0.5:0.95下降96%，主要归因于召回率和置信度的丢失。在可比指标（mAP@0.5）上，这种崩溃超过了最接近的先前基准中报告的最极端降雨退化。雾感知训练在所有严重度下均提升了检测性能（最高提升+0.320 mAP@0.5:0.95），仅导致晴空精度下降9.1%，提高了可靠检测的阈值，但并未阻止极端雾下的崩溃。由于可靠性在狭窄的能见度范围内即告丧失，简单的晴空与恶劣天气对比测试低估了操作风险。
英文摘要
Vision-based anti-UAV systems must function in poor visibility, yet most benchmarks use only clear-sky footage, and previous robustness studies treat adverse weather as a simple present/absent condition. As a result, the impact of fog severity on ground-to-air UAV detection remains poorly understood. This work presents the first severity-controlled fog benchmark for this task: synthetic fog at ten severity levels is applied to the RGB modality of the Anti-UAV300 dataset, comparing a clear-trained YOLOv5m baseline to a fog-aware model trained on both clear and foggy images. Detection performance drops sharply and non-linearly: degradation is front-loaded across light-to-moderate fog (beta approximately 0.05-0.10), with a 96% reduction in mAP@0.5:0.95 from clear to thickest fog, mainly due to lost recall and confidence. On the comparable metric (mAP@0.5), this collapse exceeds the most extreme rain degradation reported in the closest prior benchmark. Fog-aware training boosts detection across all severities (up to +0.320 mAP@0.5:0.95) with only a 9.1% drop in clear-sky accuracy, raising the threshold for reliable detection while not preventing collapse under extreme fog. Since reliability is lost within a narrow visibility range, simple clear vs adverse tests underestimate operational risk.
