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

Intermediate

RASPBERRY PI

₹5,000 – ₹15,000

3 – 6 Hours

Partial Kit

Object Detection & Classification Using Edge Impulse On Raspberry Pi

Build a live object detection and classification camera using a Raspberry Pi and Edge Impulse machine learning models.

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. 9,810.00
ESP32 CAM WiFi Module Bluetooth with OV2640 Camera Rs. 1,483.00
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

Selected: Rs. 0.00

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How It Works

This system operates by capturing live video feeds through a compatible USB or Raspberry Pi camera connected to a Raspberry Pi 5 running Raspberry Pi OS. The core of the functionality relies on the Edge Impulse Linux CLI and Node.js environment, which execute a trained machine learning model locally on the edge device. As frames are streamed via GStreamer, the Raspberry Pi processes the image data through the neural network to identify, localize, and classify specific objects in real time. Because the inference happens directly on the hardware rather than relying on a cloud server, the Raspberry Pi evaluates visual patterns and outputs classification results locally. The system can be configured and monitored by connecting a keyboard, mouse, and monitor via an HDMI interface, allowing developers to observe bounding boxes and class labels directly over the live video stream.

Why Build This

Develop practical undergraduate-level experience in deploying machine learning models directly onto resource-constrained edge hardware.

Implement real-world computer vision applications such as automated industrial sorting or live door monitoring.

Gain hands-on proficiency with Linux command-line tools, Node.js runtimes, and real-time multimedia pipelines using GStreamer.

Real-World Application

Live door monitoring for unknown visitors, industrial object sorting using robotic arms, and fruit counting on trees or conveyors.

Skills You'll Learn

Edge machine learning deployment

computer vision inference

Linux command-line interface navigation

Node.js environment setup

video stream pipeline configuration with GStreamer

peripheral interfacing

Safety Precautions

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

Technology Tags

EDGE IMPULSE

MACHINE LEARNING

PYTHON

NODE.JS

LINUX

OBJECT DETECTION

COMPUTER VISION

Frequently Asked Questions

Can I use a different model training platform besides Edge Impulse?

While this specific project is structured around the Edge Impulse Linux CLI for streamlined deployment, the hardware and software stack can theoretically support other exported embedded ML frameworks if manually configured.

Why is a 5V power adapter with a USB Type-C connector required?

The Raspberry Pi 5 has higher power demands than previous iterations, requiring a stable, high-current 5V USB-C power supply to prevent unexpected reboots during heavy machine learning inference.

Is an internet connection required to run the object detection once deployed?

No, once the Edge Impulse model and necessary software packages are downloaded and set up on the Raspberry Pi, the object detection and classification inference runs entirely offline on the edge.

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-00677