dida at the PyData Berlin meetup: anomaly detection in track scenes

On November 20th, Maximilian Trescher presented our work on "Anomaly Detection in Track Scenes" at PyData Berlin 2024.

In collaboration with Deutsche Bahn as part of the Digitale Schiene Deutschland initiative, we developed a machine learning solution to detect hazardous and anomalous objects on train tracks using onboard RGB cameras. The system uniquely identifies not just predefined classes but ranks objects by anomaly level.

Maximilian highlighted the challenges we faced, the development of our multi-component pipeline—featuring depth estimation, segmentation, and anomaly detection—and the use of the OSDAR23 dataset alongside self-supervised learning.

We are grateful to PyData Berlin for the opportunity to share this project with the community. Thank you to everyone who attended the talk and contributed to the engaging discussions that followed.