TWIST: Trains under Weather, Illumination, and Seasonal Time

This repository accompanies the paper: TWIST: Trains under Weather, Illumination, and Seasonal TimeMomin Ali, Andre Stenger, Til Arkenberg, Laura Harms, Olaf LandsiedelISIoT Workshop at DCOSS 2026 TWIST is a real-world dataset for train detection designed to improve robustness of vision-based railway monitoring systems under diverse environmental conditions. Vision-based railway monitoring systems often fail in real-world deployments due to limited training data diversity. TWIST addresses this gap by providing a dataset collected across multiple seasons, capturing: 🌧️ Rain ❄️ Snow 🌫️ Fog 🌙 Low-light & night ☀️ Glare & daylight 🚄 Motion blur & varying train speeds Check out the supplied Jupyter Notebooks to analyze the data in our TWIST GitHub repo 📊 Dataset Statistics Total Images: ~38,000 Resolution: 640 × 480 Binary Annotations: 10,000 Detailed Annotations: 1,493 images Label Types Binary Labels Train / No Train Detailed labels Locomotive Wagon Freight Car High-Speed Train 🏷️ Annotation Format Annotations follow the YOLO format: <class_id> <x_center> <y_center> <width> <height> All values are normalized between 0 and 1 Compatible with YOLOv5, YOLOv8, and other frameworks This project is licensed under the terms of the Creative Commons Attribution 4.0 International License.