| --- |
| license: cc-by-nc-4.0 |
| tags: |
| - autonomous-driving |
| - object-detection |
| - open-set-recognition |
| - functional-safety |
| - neuro-symbolic |
| pipeline_tag: object-detection |
| --- |
| |
| # HOWC : 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](https://doi.org/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. HOWC 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): |
|
|
| ```bash |
| 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. |
|
|
