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The LYNRED Mobility Dataset Multimodal Detection Subset (LYNRED-MDS) introduces 4000 RGB-infrared image pairs captured under diverse weather and lighting conditions to enhance early collision prediction in low-visibility scenarios. This dataset addresses the limitations of existing benchmarks, which primarily focus on clear weather and simplistic scenarios, thereby providing a more comprehensive resource for training and evaluating advanced driver-assistance systems. Thermal cross-dataset evaluations using a YOLOv8n baseline indicate that LYNRED-MDS significantly improves generalization for pedestrian detection in complex driving environments.
Early collision prediction in low-visibility conditions can be dramatically improved with the LYNRED-MDS, which captures the complexities of real-world 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 low-visibility 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. IEEE SOCIETY/COUNCIL Signal Processing Society (SPS) DATA TYPE/LOCATION Thermal images, Visible images, Multispectral images; Grenoble, Auvergne-Rh么ne-Alpes, FRANCE DATA DOI/PID https://www.lynred.com/lynred-mobility-dataset
