Project Hub /Artificial Intelligence & Edge ML/Object Detection & Tracking/DIY Object Classification Using Edge Impulse & Raspberry Pi

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RASPBERRY PI

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3 – 6 Hours

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DIY Object Classification Using Edge Impulse & Raspberry Pi

Learn how to build a DIY object classification and detection system using a Raspberry Pi and the cloud-based Edge Impulse platform.

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.

Raspberry Pi 3 - Model B+ 1.4Ghz 64-bit quad-core Processor with 1 GB Ram (Original) Rs. 5,400.00
ESP32 CAM WiFi Module Bluetooth with OV2640 Camera Rs. 810.00
MB102 Adjustable Breadboard Power Supply Module (3.3V & 5V) for Arduino & Raspberry Pi Rs. 139.00
Jumper Wire 10 Pin Male To Male 20cm Rs. 45.00
400 Points Mini Solderless Prototype PCB Breadboard High Quality Rs. 89.00
Jumper Wire 10 Pin Male To Female 20cm Rs. 45.00

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How It Works

The DIY object classification system operates by capturing visual data through a connected USB camera linked to a Raspberry Pi 4 Model B running Raspberry Pi OS. During the initial development phase, the system is connected to a monitor, keyboard, and mouse to allow the user to interact with the desktop environment and install required dependencies such as Node.js and the Edge Impulse Linux CLI. Using an SD card flashed via Raspberry Pi Imager, the Pi interfaces with the cloud-based Edge Impulse Web Platform to collect and upload training images. Once the machine learning model is trained in the cloud, it is downloaded back to the Raspberry Pi for local inference. During live execution, the USB camera streams real-time video frames into the locally running Edge Impulse model, which processes the pixel data to classify objects according to the trained categories. The entire system is powered by a dedicated 5V power adaptor with a USB Type-C connector, ensuring stable operation during intensive processing tasks.

Why Build This

Design and deploy custom computer vision models at the edge using real-time sensor data from a USB camera.

Integrate cloud-based machine learning workflows with resource-constrained local hardware like the Raspberry Pi.

Implement practical automated monitoring and sorting solutions applicable to industrial robotics and security systems.

Real-World Application

Live entrance door monitoring, unknown person alerts, industrial object classification and separation using robotic arms, and fruit counting on trees or machine separators.

Skills You'll Learn

Edge machine learning deployment

computer vision data collection

Linux command-line interface navigation

Node.js environment setup

embedded system inference

Safety Precautions

No specific precautions noted for this build — always follow general electronics safety practices.

Technology Tags

EDGE IMPULSE

ARTIFICIAL INTELLIGENCE

MACHINE LEARNING

COMPUTER VISION

RASPBERRY PI

PYTHON

NODE.JS

Frequently Asked Questions

Can I use a Raspberry Pi 3 B instead of a Raspberry Pi 4 Model B?

Yes, the project data notes compatibility with the Raspberry Pi 3 B, though processing speeds for machine learning inference may vary compared to the Raspberry Pi 4.

Do I need a permanent keyboard and monitor attached to the Raspberry Pi?

No, a keyboard, monitor, mouse, and HDMI cable are primarily required during the initial setup and configuration phase, after which the system can potentially be run headlessly.

How does the Raspberry Pi get its machine learning model?

You collect image data using the USB camera and Edge Impulse tools, train the model on the cloud-based Edge Impulse Web Platform, and then download the compiled model to run locally on the Raspberry Pi.

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.

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