Detecting Workers Without Helmet Using YOLO V5 And MaixCam

In industrial settings, safety helmets are a critical element of personal protective equipment (PPE), acting as the final barrier when other measures fail. Safety departments enforce PPE rules across process plants, especially among large numbers of contract workers. While training builds hazard awareness, studies show supervision plays a stronger role in accident prevention.

To address this need more effectively, a technology-led solution replaces manual oversight with an intelligent, machine-based system.

A high-fidelity camera continuously monitors the approach area. If a worker without a helmet is detected, the system triggers an instant alert—either a cautionary alarm or a verbal instruction to comply. A single camera can cover large zones, ensuring round-the-clock, impartial enforcement. Once a violation is flagged, further action—such as activating an audio alert or controlling a gate—is handled by a microprocessor-based setup.

The solution uses MaixCam’s GPIO library to manage these automated responses, delivering scalability and cost-efficiency. Acting as a tireless, unbiased supervisor, this AI-powered safety system reduces reliance on human monitoring while boosting compliance. A prototype is shown in Fig. 1.

The required components are listed in the Bill of Materials table. An LED or buzzer may be added for alerts, though not included in the Bill of Materials.

Since the same board is used in this solution, refer to ‘ Face Recognition Using YOLO ,’ published in the April issue. That article provides additional details regarding the MaixCam development board, including its usage and installation, which should be reviewed before proceeding.

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Detailed Project Description

Detecting Workers Without Helmet Using YOLO V5 And MaixCam applies computer vision to camera frames to detect or recognise what is in view and act on the result automatically.

How It Works
1. The camera captures frames continuously.
2. Frames are pre-processed and fed to the detection/recognition model.
3. Detections are drawn on the output and used to trigger the required action (log, alert, unlock, count).
4. Results and images are stored or streamed to a browser/dashboard.

Key Components and Their Role
- MaixCAM: AI camera module with an on-board NPU that runs the vision model at the edge.
- Bluetooth Low Energy: short-range low-power wireless link to a phone or browser.
- Camera module: image sensor used for capture, streaming or computer vision.
- Buzzer: audible alert output.
- YOLO: real-time object-detection model family.

Implementation Steps
1. Collect the components and study each module's datasheet (pin-out, voltage, interface).
2. Wire the circuit on a breadboard following the pin mapping; power sensors from the correct rail (3.3 V / 5 V).
3. Install BLE, CAN and the required libraries; flash a minimal test sketch for each module separately.
4. Write the main firmware: read sensors → process → drive outputs / send data.
5. Configure network or cloud credentials and test communication end-to-end.
6. Calibrate thresholds, add error handling (reconnect, sensor time-out) and run a long-duration test.
7. Finalise on a PCB / enclosure and document wiring, code and results.

Key Features
- Real-time detection
- Annotated output
- Event logging
- Adjustable confidence threshold

Expected Output
On completion the student will have a live video/image feed with detected objects highlighted and the corresponding actions logged.

Deliverables
- Complete source code with comments
- Circuit diagram and pin mapping
- Project report with screenshots and results
- Demonstration-ready build