# Descriptor: LYNRED Mobility Dataset Multimodal Detection Subset (LYNRED-MDS)

arXiv: https://arxiv.org/abs/2607.01871 · HTML: https://mlsanity.com/paper/2607.01871/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 2 Jul 2026.

## Builds on (most cited first, 11 of 11)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection](https://mlsanity.com/paper/2203.16220/) (2022)
- [LLVIP: A Visible-infrared Paired Dataset for Low-light Vision](https://mlsanity.com/paper/2108.10831/) (2021)
- [Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks](https://mlsanity.com/paper/2009.12664/) (2020)
- [Multispectral Deep Neural Networks for Pedestrian Detection](https://mlsanity.com/paper/1611.02644/) (2016)
- [Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation](https://mlsanity.com/paper/1808.04818/) (2018)
- [RGB-X Object Detection via Scene-Specific Fusion Modules](https://mlsanity.com/paper/2310.19372/) (2023)
- [AMFD: Distillation via Adaptive Multimodal Fusion for Multispectral Pedestrian Detection](https://mlsanity.com/paper/2405.12944/) (2024)
- [InfraParis: A multi-modal and multi-task autonomous driving dataset](https://mlsanity.com/paper/2309.15751/) (2023)
- [Optimizing the image correction pipeline for pedestrian detection in the thermal-infrared domain](https://mlsanity.com/paper/2407.04484/) (2024)

## Abstract (arXiv)

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.