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.