Work

LDFA Anonymization

Computer Vision
PyTorch
SAM
MLOps

Fine-tuned Meta's SAM for German license plate detection in self-driving applications. Improved accuracy from 75% to 95%, reduced false positives by 15%. W&B, Flux Inpainting. FZI Forschungszentrum Informatik (Karlsruhe, remote).

LDFA license plate detection and anonymization for autonomous driving

At FZI Forschungszentrum Informatik I worked on the LDFA anonymization pipeline for self-driving data: detecting German license plates and anonymizing them before storage or sharing.

Approach

  • Segment Anything (SAM): Fine-tuned Meta’s SAM for license plate regions in our datasets.
  • Metrics: Lifted detection accuracy from 75% to 95% and cut false positives by 15%.
  • Synthetic data: Used Flux Inpainting to generate synthetic plates for training and augmentation.
  • Experiment tracking: W&B for runs, hyperparameters, and model versions.

The pipeline is used in research and data preparation for autonomous driving at FZI.