Project Hub /Artificial Intelligence & Edge ML/Object Detection & Tracking/AI Helmet Detection System Using YOLO V5 And MaixCam

Advanced

MAIXDUINO

₹5,000 – ₹15,000

3 – 6 Hours

Partial Kit

AI Helmet Detection System Using YOLO V5 And MaixCam

An automated AI-powered workplace safety system using MaixCam and YOLO V5 to detect workers without safety helmets and trigger instant alerts.

Originally published on ElectronicsForU

View original tutorial

Parts You'll Need

This project is sourced from another site, not written by us — this list is our best match of their components against what we stock, not a verified bill of materials. Always check the original blog (linked above) for the actual parts list before buying.

MB102 Adjustable Breadboard Power Supply Module (3.3V & 5V) for Arduino & Raspberry Pi Rs. 139.00
10pcs 5mm RED Diffused Light Emitting Diodes - LEDs Set Rs. 39.00
400 Points Mini Solderless Prototype PCB Breadboard High Quality Rs. 89.00
Jumper Wire 10 Pin Male To Male 20cm Rs. 45.00
Jumper Wire 10 Pin Male To Female 20cm Rs. 45.00

Selected: Rs. 0.00

We don't carry yet: Other Dev Board (Indusboard / Maixduino / misc)

Browse all components

How It Works

The AI Helmet Detection System leverages the MaixCam edge vision device running a specialized YOLO V5 object detection model to monitor a workspace in real-time. The MaixCam captures video frames of individuals entering or working within the camera's field of view. The embedded YOLO V5 neural network processes these frames locally on the device to classify whether workers are wearing required safety helmets. Power is supplied to the system via a 5V DC power adaptor, ensuring stable continuous operation of the vision board. If the model detects a worker without a helmet, the MaixCam processes this inference result through its internal logic and utilizes its GPIO library to trigger an optional external LED or buzzer connected to the output pins. This immediate hardware-level activation serves as a local warning signal or indicator for safety compliance enforcement.

Why Build This

Automate personal protective equipment (PPE) compliance monitoring in industrial plants and construction sites.

Gain practical experience deploying custom edge machine learning models like YOLO V5 onto embedded hardware vision boards.

Implement physical computing outputs by interfacing general-purpose input/output (GPIO) pins with alert peripherals like LEDs and buzzers.

Real-World Application

Enforcing Personal Protective Equipment (PPE) compliance in industrial process plants, manufacturing units, and construction sites to prevent workplace accidents.

Skills You'll Learn

Edge AI model deployment

YOLO V5 object detection

embedded GPIO programming

real-time computer vision

hardware-software integration

Safety Precautions

Ensure proper electrical isolation when connecting external buzzers or LEDs to the MaixCam GPIO pins.

Technology Tags

MACHINE LEARNING

YOLO V5

MAIXCAM

COMPUTER VISION

OBJECT DETECTION

SAFETY SYSTEMS

PYTHON

EMBEDDED SYSTEMS

Frequently Asked Questions

Can the MaixCam run the YOLO V5 model completely offline without an internet connection?

Yes, the MaixCam is designed for edge machine learning, meaning all YOLO V5 object detection inferences run locally on the device's built-in processor without requiring cloud connectivity.

What kind of power supply is required for the MaixCam?

The system operates using a standard 5V DC power adaptor to ensure stable and continuous power for image processing and edge computing tasks.

How are the optional alert devices like a buzzer or LED connected?

External indicators can be connected directly to the MaixCam GPIO pins, allowing the system to trigger physical alerts based on the results of the AI model's detections.

Gallery

This page is part of Kitkraft's Project Hub — a curated collection of electronics & robotics projects gathered from the maker community to help students and hobbyists discover and learn to build. Kitkraft does not claim authorship of the original project, and makes no representation as to the accuracy, completeness or safety of third-party source material. The summary and "How It Works" explanation above are AI-generated, rewritten with reference to the original tutorial — not a reproduction of it. Full credit and a direct link to the original source are provided above. If you're the original creator and have a concern about how your work is represented here, please contact us and we'll address it promptly.

Kitkraft Project Hub — curated from the maker community, credited at the source.

PRJ-01192