hardhat-setup

name	hardhat-setup
description	Train and deploy a hard hat detection ML model using Edge Impulse on Jetson Nano/NX/AGX. Covers Edge Impulse project setup, data collection (public datasets, PC camera, or Jetson camera), model training, and deployment via Edge Impulse CLI or Linux Python SDK.

Hard Hat Detection with Edge Impulse on Jetson

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Execution model

Run one phase at a time. After each phase:

• Relay all command output to the user.
• If output contains [STOP] → stop immediately, consult the failure decision tree below.
• If output ends with [OK] → tell the user "Phase N complete" and proceed to the next phase.

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Prerequisites

Requirement	Details
Hardware	NVIDIA Jetson Nano, Xavier NX, or Xavier AGX
Peripherals	USB camera, HDMI display, keyboard, mouse
Account	Edge Impulse account (https://studio.edgeimpulse.com)
Network	Internet connection on both PC and Jetson
Software	Ubuntu on Jetson, Edge Impulse CLI

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Phase 1 — Create Edge Impulse project (~2 min)

1. Register/login at https://studio.edgeimpulse.com
2. Click "Create new project", name it "Hard hat detection"
3. Select "Image" as the data type
4. Set configuration to "Classify multiple objects (object detection)"

[OK] when the project dashboard is visible.

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Phase 2 — Collect and label data (~15–30 min)

Choose one of three data collection methods:

Option A — Upload public datasets:

• Download from Flickr-Faces-HQ Dataset (https://github.com/NVlabs/ffhq-dataset)
• Upload via "Data acquisition" → "Upload data" in Edge Impulse

Option B — PC camera:

• From Dashboard, click "LET'S COLLECT SOME DATA" → select computer
• Grant camera access, capture images
• Label as "Hard Hat" and "Head"

Option C — Jetson camera:

• Connect Jetson to Edge Impulse:

ping -c 3 www.google.com
edge-impulse-linux

• Select USB camera, name the device
• Capture and label images from the "Data acquisition" page

After collection, go to "Labeling queue" and draw bounding boxes around heads. Label as "Hard Hat" or "Head".

[OK] when labeled data appears in Data acquisition. [STOP] if Jetson can't connect to Edge Impulse.

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Phase 3 — Train the model (~10–30 min)

1. Go to "Impulse design" → add image processing block and object detection learning block → Save impulse
2. Click "Image" → configure as "RGB" → "Save Parameters" → "Generate features"
3. Click "Object detection" → "Start training"
4. When training completes, click "Model testing" to evaluate

[OK] when model testing shows detection results.

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Phase 4 — Deploy to Jetson (~5 min)

Option A — Edge Impulse CLI runner:

Ensure Jetson is connected to Edge Impulse (see Phase 2 Option C), then:

edge-impulse-linux-runner

Copy the displayed URL and open in a browser to see live detection.

Option B — Linux Python SDK:

sudo apt-get install libatlas-base-dev libportaudio2 libportaudiocpp0 portaudio19-dev
pip3 install edge_impulse_linux

Install Edge Impulse CLI for Linux:

sudo apt install python3.7-dev
wget -q -O - https://cdn.edgeimpulse.com/firmware/linux/jetson.sh | bash

Download the model:

edge-impulse-linux-runner --download modelfile.eim

Run the detection script:

python3 hardhat_detectation.py /home/jetson-nano/modelfile.eim

[OK] when detection output shows "Hard Hat" or "Head" labels on the display.

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Failure decision tree

Symptom	Action
edge-impulse-linux command not found	Install Edge Impulse CLI: wget -q -O - https://cdn.edgeimpulse.com/firmware/linux/jetson.sh | bash
Jetson can't reach Edge Impulse	Check internet with ping -c 3 www.google.com. Verify DNS settings.
Camera not detected	Ensure USB camera is connected. Try a different USB port. Check with ls /dev/video*.
Training accuracy is low	Collect more data. Ensure balanced labels. Improve labeling quality with tight bounding boxes.
edge-impulse-linux-runner fails	Re-run edge-impulse-linux to reconnect. Check account credentials.
Model download fails	Add --clean flag to switch projects. Check internet connectivity.
Python SDK import errors	Ensure libatlas-base-dev and other dependencies are installed. Reinstall edge_impulse_linux.
Detection display not showing	Verify camera is accessible. Check the browser URL from the runner output.

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Reference files

• references/source.body.md — full original Seeed tutorial with step-by-step screenshots, Python SDK code, and deployment details (reference only)