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Saqr — PPE Safety Detection on Unitree G1

Real-time PPE compliance (helmet, vest, boots, gloves, goggles) using YOLO11n, designed to run on a Unitree G1 humanoid with an Intel RealSense D435I. On UNSAFE the robot speaks a warning and plays the reject arm action.

Layout

saqr/           # python package
  core/         # detection + tracking + events (shared by CLI/GUI/bridge)
  apps/         # CLI entry points (saqr, detect, train, manager, view_stream)
  gui/          # PySide6 desktop GUI
  robot/        # G1 bridge + DDS controller
  utils/        # logger
scripts/        # deploy.sh, start_saqr.sh, run_local.sh, run_robot.sh, systemd unit
config/         # logging.json
data/           # dataset/, models/   (gitignored)
runtime/        # captures/, logs/, runs/   (gitignored)
docs/           # DEPLOY.md, start.md, use_case_catalogue.pdf

Quick start

# Install the package (editable)
pip install -e .

# Local dev run (webcam)
saqr --source 0

# PySide6 GUI
pip install -e ".[gui]"
saqr-gui

# On the Unitree G1 (bridge owns the R2+X / R2+Y flow)
saqr-bridge --iface eth0 --source realsense --headless -- --stream 8080

Without installing, everything still works via python -m:

python -m saqr.apps.saqr_cli --source 0
python -m saqr.robot.bridge --iface eth0 --source realsense --headless

Docs

Data & Models

The data/ and runtime/ directories are excluded from git (too large). Download them separately before training or running inference.

data/ — dataset and pre-trained weights

Expected contents:

data/
  dataset/
    train/{images,labels}/
    valid/{images,labels}/
    test/{images,labels}/
    data.yaml
  models/
    saqr_best.pt       # Saqr YOLO11n fine-tuned on PPE
    saqr_last.pt
    yolo11n.pt         # base YOLO11n
    yolo26n.pt         # base YOLO26n

Download:

  • Dataset (PPE, Roboflow): testcasque/ppe-detection-qlq3d Open the Roboflow link → Download Dataset → format YOLOv11 → unzip into data/dataset/.
  • Base YOLO weights: Ultralytics assets releases Grab yolo11n.pt (and optionally yolo26n.pt) into data/models/.
  • Saqr fine-tuned weights (saqr_best.pt, saqr_last.pt): Produced by training — see "Training" below. Or request from the maintainer.

Place everything under data/ so the tree matches above.

runtime/ — training output (optional)

Auto-generated when you run training. Not required for inference. Contains confusion matrices, PR curves, batch previews, and the raw weights under runtime/runs/train/saqr_det/weights/.

Training

# after placing the dataset in data/dataset/ and base weights in data/models/
python -m saqr.apps.train_cli --data data/dataset/data.yaml --weights data/models/yolo11n.pt

Outputs land in runtime/runs/train/saqr_det/. Copy the best checkpoint to data/models/saqr_best.pt to use it at inference time.