YOLO+ : hierarchical taxonomic perception for novel road objects
A training-free layer that turns a flat object detector into a hierarchical, open-set one. Each detection is classified by taxonomic abstraction: the most specific level the evidence safely supports, or an explicit UNKNOWN OBSTACLE, never a confident wrong leaf.
Paper (open access): Hierarchical Taxonomic Abstraction for the Safe Handling of Novel Objects in Autonomous Driving Perception, F. Schaller, doi:10.5281/zenodo.21593472. Source & full history: https://github.com/freshNfunky/IE2025-Research-Paper.
Why it is different
A flat detector returns one fixed class or nothing. On an untrained object (a horse-drawn carriage, an overloaded truck) it must mislabel it or drop it, both unsafe. YOLO+ abstracts up a taxonomy to a still-useful category (… → Truck → Transport Vehicle → Vehicle), bounded by a per-branch safety floor so it never collapses into a useless "Object"; below the floor it flags an explicit UNKNOWN OBSTACLE with an inspectable decision path.
Honest scope
- Not new weights, and not a closed-set-accuracy win: on COCO mAP a trained YOLO is more accurate. The contribution is the taxonomic abstraction layer over open-vocabulary (CLIP) features.
- Where it wins: on known objects, 0% categorical (off-branch) errors with ~24% calibrated abstention, vs a flat head's ~53% off-branch errors; on novel objects, a safe coarse label or a flagged UNKNOWN instead of a confident wrong leaf.
- Training-free (pretrained YOLO + CLIP zero-shot). First run downloads weights (~360 MB).
Run it locally
This repository is self-contained (code + taxonomy + a Gradio app):
pip install -r requirements.txt
python app.py # Gradio UI: upload an image, see the taxonomy decision
python app.py --share # same, but also prints a temporary public URL (~72h)
License
CC BY-NC 4.0, matching the paper.