发表机构
Université Clermont Auvergne; Clermont Auvergne INP; CNRS; Institut Pascal（克莱蒙奥弗涅大学; 克莱蒙奥弗涅国立理工学院; 法国国家科学研究中心; 帕斯卡研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究在RASMD数据集上系统比较了RGB与SWIR在自动驾驶目标检测中的性能，提出传感器优势挖掘框架，发现SWIR在眩光、水滴、低对比度和远距离车辆等安全关键场景中具有互补优势。
AI 中文摘要
短波红外（SWIR）成像已成为自动驾驶领域一种有前景的传感模态，然而与RGB相比，其在实际应用中的优势在不同条件下仍缺乏系统性的表征。本文在RASMD数据集上对配对的RGB和SWIR目标检测进行了系统的比较研究，涵盖了四种天气条件和两种实时检测架构，并针对统一的地面真值评估了多种微调方案。总体而言，RGB在大多数场景中表现出相当或更优的性能，而RF-DETR在不同条件下展现出更强的鲁棒性。除了总体指标外，我们提出了一种传感器优势挖掘框架，该框架结合多模型一致性分析与有针对性的手动检查，利用大量未标注的配对数据来识别某一传感模态能提供更可靠检测的场景。分析表明，SWIR在四种安全关键情境中具有明显优势，包括挡风玻璃眩光、挡风玻璃上的水滴、低对比度物体可见性以及远距离车辆检测。研究结果表明，SWIR应被视为一种互补模态，可在罕见但具有挑战性的条件下增强感知能力。数据集可根据要求提供，所有代码和训练好的模型权重均已在此https URL公开发布。
英文摘要
Short-wave infrared (SWIR) imaging has emerged as a promising modality for autonomous driving, yet its practical benefits over RGB remain poorly characterized across diverse conditions. This paper presents a systematic comparative study of paired RGB and SWIR object detection on the RASMD dataset, covering four weather conditions and two real-time detection architectures, with various fine-tunings evaluated against a unified ground truth. Overall, RGB demonstrates comparable or superior performance in most scenarios, while RF-DETR exhibits greater robustness across varying conditions. Beyond aggregate metrics, we propose a sensor-dominance mining framework that combines multi-model agreement with targeted manual inspection to identify scenarios where one sensing modality provides more reliable detections using largely unannotated paired data. This analysis reveals that SWIR offers clear advantages in four safety-critical situations, including windshield glare, water droplets on the windshield, low-contrast object visibility, and long-range vehicle detection. The findings suggest that SWIR should be viewed as a complementary modality that enhances perception in rare but challenging conditions. The datasets will be available upon request, and all code and trained model weights are publicly released at https://github.com/comsee-research/swir-adverse-env-analysis.
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