构建多模态交通检测数据集，助力夜间雾天智能驾驶感知
Descriptor: LYNRED Mobility Dataset Multimodal Detection Subset (LYNRED-MDS)
- 采集4000组真实道路的RGB与热成像图像对
- 覆盖多种天气光照条件，支持行人检测泛化能力评估
- 适合研发高可靠性自动驾驶感知系统的研究者使用
当前道路安全系统主要关注碰撞后的损伤控制，但算法感知的进步正推动早期碰撞预测的发展，尤其在夜间或雾天等低能见度场景下，热红外感知优于人眼和可见光成像。现有如FLIR ADAS、LLVIP等RGB-红外数据集多为晴朗天气、场景简单。本文提出LYNRED-MDS：LYNRED Mobility Dataset的多模态检测子集，包含在法国格勒诺布尔地区采集的4000组RGB-红外图像对，涵盖城市、乡村、山地等多种驾驶环境及符合西欧标准的车辆车队。基于YOLOv8n的跨数据集热成像评估表明，该数据集具备良好的行人检测泛化潜力。通过覆盖关键边缘案例，支持更可靠、可部署的高级驾驶辅助系统视觉系统研发。
原文摘要 · Abstract (English)
Current road safety systems primarily focus on minimizing post-collision damage. However, advances in algorithmic perception are shifting focus toward early collision prediction, especially in lowvisibility conditions like nighttime or fog, where thermal infrared sensing outperforms both human vision and RGB imaging. While available RGB-infrared datasets such as FLIR ADAS and LLVIP are good benchmarks, they mostly consist of clear weather and overly simple scenarios. In this article, we introduce the LYNRED-MDS: Multimodal Detection Subset, a subset of the LYNRED Mobility Dataset, comprised of 4000 RGB-infrared image pairs captured under diverse weather, lighting, and road conditions around Grenoble, France. Our dataset spans varied driving contexts (urban, rural, mountainous, etc.) and a vehicle fleet compliant with Western European standards. Thermal cross-dataset evaluation using a YOLOv8n baseline suggests that our dataset offers strong generalization potential for pedestrian detection in driving scenarios. By covering critical edge cases, our dataset supports the development of more reliable and deployable vision systems for advanced driver-assistance systems.
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