Descriptor: LYNRED Mobility Dataset Multimodal Detection Subset (LYNRED-MDS)
Provides a more challenging and realistic benchmark for thermal-RGB pedestrian detection in autonomous driving, addressing edge cases like low visibility.
The paper introduces LYNRED-MDS, a multimodal RGB-infrared dataset of 4000 image pairs for pedestrian detection in diverse weather and lighting conditions. Thermal cross-dataset evaluation with YOLOv8n shows strong generalization potential for driving scenarios.
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.