构建首个覆盖七类极端天气的交通目标检测基准，助力自动驾驶抗恶劣天气能力提升。
XWOD: A Real-World Benchmark for Object Detection under Extreme Weather Conditions
- 构建包含10,010张图像、42,924个标注的多天气真实场景数据集
- 在多个外部基准上实现比现有方法高35%-83%的检测性能提升
- 首次涵盖洪水、龙卷风、野火等气候加剧型灾害，适合自动驾驶安全研究
自动驾驶与智能交通系统在极端天气下仍易失效。美国联邦公路管理局数据显示，每年约74.5万起事故和3,800起死亡与天气有关，近期监管调查也关注了高等级自动驾驶系统在低能见度下的失败问题。然而，现有用于评估天气鲁棒性的数据集在规模、多样性和真实性方面仍显不足。本文提出XWOD（Extreme Weather Object Detection），一个大规模真实世界交通目标检测基准，包含10,010张图像和42,924个边界框，覆盖雨、雪、雾、霾/沙/尘、洪水、龙卷风、野火七种极端天气条件，涵盖车、人、卡车、摩托车、自行车、公交六类交通物体。该数据集将天气分类从单一扩展至七类，首次涵盖气候加剧型灾害。通过在XWOD上训练标准YOLO模型，并在外部天气基准零样本测试，其在RTTS、DAWN、WEDGE上的mAP₅₀分别达到63.00%、59.94%、61.12%，相较对应基线提升56%、83%、35%。跨数据集表现表明XWOD是学习天气鲁棒感知的优质源域。数据集、划分、基线权重及可复现评估代码已开源。
原文摘要 · Abstract (English)
Autonomous driving and intelligent transportation systems remain vulnerable under extreme weather. The U.S. Federal Highway Administration reports that roughly 745,000 crashes and 3,800 fatalities per year are weather-related, and recent regulatory investigations have examined failures of Level-2/3 driving systems under reduced-visibility conditions. However, datasets commonly used to evaluate weather robustness remain limited in scale, diversity, and realism. In this paper, we introduce XWOD (Extreme Weather Object Detection), a large-scale real-world traffic-object detection benchmark containing 10,010 images and 42,924 bounding boxes across seven extreme weather conditions: rain, snow, fog, haze/sand/dust, flooding, tornado, and wildfire. The dataset covers six traffic-object categories, including car, person, truck, motorcycle, bicycle, and bus. XWOD extends the weather taxonomy from one to seven conditions, and is the first to cover the emerging class of climate-amplified hazards, such as flooding, tornado, and wildfire. To evaluate the quality of our data, we train standard YOLO-family detectors on XWOD and test them zero-shot on external weather benchmarks, achieving mAP$_{50}$ scores of 63.00% on RTTS, 59.94% on DAWN, and 61.12% on WEDGE, compared with the corresponding published YOLO-based baselines of 40.37%, 32.75%, and 45.41%, respectively, representing relative improvements of 56%, 83%, and 35%. These cross-dataset results show that XWOD provides a strong source domain for learning weather-robust traffic perception. We release the dataset, splits, baseline weights, and reproducible evaluation code under a research-use license.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。