---
license: apache-2.0
tags:
- object-detection
- helmet-detection
- yolo
- image-classification
- cctv
- anpr
- india
library_name: ultralytics
pipeline_tag: object-detection
---

# Helmet v5 — Indian CCTV Helmet + ANPR pipeline

End-to-end pipeline for detecting motorcycle riders **without helmets** and reading their **license plates** from Andhra Pradesh RTGS CCTV feeds. Built to match / beat Videonetics commercial ANPR on the same footage.

## What's in this repo

| Component | File | Purpose |
|---|---|---|
| Motorcycle + person detector | `models/yolo11l_cctv_ft.pt` | YOLO11l fine-tuned on 627 pseudo-labeled CCTV frames. **mAP50 = 0.979, mAP50-95 = 0.917** |
| Head helmet classifier | `models/helmet_head_v2.pt` | EfficientNet-B0 on driver head crops, 2-class. **Val F1 = 0.864** |
| Plate detector | `models/plate_yolo_l.pt`, `plate_yolo_ft.pt` | YOLO11l stock + fine-tuned for Indian plate bboxes |
| Plate OCR | (TrOCR v1/v2/v3 on HF model repo — not bundled, train via `tools/train_plate_ocr_v3.py`) | |
| Pose model | `yolo11l-pose.pt` | Stock Ultralytics pose — localizes driver head keypoints |

## Pipeline (`tools/analyze_tripwire.py`)

1. YOLO fine-tuned detects motorcycle + person boxes per frame
2. Assign nearest person to each motorcycle as **driver** (single rider per bike — pillions ignored)
3. YOLO11l-pose finds driver head keypoints (nose/eyes/ears) → aspect-preserved 224×224 crop
4. EfficientNet-B0 classifier: `helmet` vs `no_helmet` (multi-frame voting, MIN_VOTES=2)
5. Plate detector + TrOCR on plate crop
6. `postprocess_dedup.py` — plate-string dedup + time-window dedup for no-plate events

## Training scripts (reproducible)

- `tools/extract_ft_frames.py` — clip-level train/val split, variance-filter corrupted HEVC frames
- `tools/autolabel_frames.py` — YOLO11l + YOLO11x ensemble pseudo-labeling, cross-model NMS @ conf≥0.7
- `tools/train_yolo_ft.py` — 50 epochs, imgsz=1280, cosine LR, close_mosaic=10, patience=10
- `tools/build_head_dataset.py` — pose-extracted head crops, source-level split by eventId
- `tools/train_head_classifier.py` — EfficientNet-B0 + PadResize, class-weighted CE, F1 early-stop
- `tools/train_plate_ocr_v3.py` — TrOCR fine-tune on Indian plate regex `^[A-Z]{2}\d{2}[A-Z]{1,3}\d{3,4}$`

## Results on `ch284_20260421_120655` (15-min clip)

Reference: Videonetics reported 77 no-helmet events. v5 matches after dedup.

## Status

Last trained 2026-04-22. YOLO reached mAP50=0.979 at ep50; head classifier early-stopped at ep7 (F1=0.864).

Not included: `data/` (30GB raw RTGS videos — proprietary), TrOCR checkpoints (~7GB — train via `tools/train_plate_ocr_v3.py`).

Datasets: [vivekvar/cctv-datasets](https://huggingface.co/datasets/vivekvar/cctv-datasets).