Machine Vision Bundle
A rapid solution for embedded machine learning (ML) combining vision, audio and connectivity. Open the lenses to a new territory of lean and efficient image processing applications for your Arduino projects.
Overview
The Arduino Portenta Vision Shield is a production-ready expansion for the powerful Arduino Portenta H7. It adds a low-power camera, two microphones, and connectivity; everything you need for the rapid development of edge ML applications.
The Portenta H7 simultaneously runs high-level code along with real-time tasks. The
H7's main processor is a dual-core STM32H747 including a Cortex® M7 running at 480MHz and a Cortex® M4 running at 240MHz. The two cores communicate via a Remote Procedure Call mechanism that allows seamless calling of functions on the other processor.
Both processors share all the in-chip peripherals and can run:
- Arduino sketches on top of the Arm® Mbed™ OS
- Native Mbed™ applications
MicroPython / JavaScript (via an interpreter)
TensorFlow™ Lite
Moreover, the onboard wireless module allows the simultaneous management of WiFi and Bluetooth® connectivity on the Portenta H7.
The Portenta Vision Shield brings industry-rated vision and audio capabilities to your Portenta H7. This hardware add-on lets you run embedded computer vision applications, connect wirelessly or via Ethernet to the Arduino Cloud or your own infrastructure, and activate your system using sound detection.
The Vision shield comes with a 324x324 pixels camera module that contains an ultra low power image sensor designed for always-on vision devices and applications. The high sensitivity image sensors can capture gestures, ambient light, proximity sensing and object identification.
There’s a plethora of applications you can deploy with Portenta H7 and Portenta Vision Shield. The demonstration below shows how to implement an accurate digits recognition system using Edge Impulse. Digits recognition using computer vision is desirable in many application and market areas, such as grocery retail, manufacturing, utility metering, and administration.
Learn how to implement accurate digit recognition using Edge Impulse.
Check out the documentation to easily implement Portenta H7 and Portenta Vision Shield in your projects.
Arduino IoT Cloud Compatible
Use your MKR board on Arduino's IoT Cloud, a simple and fast way to ensure secure communication for all of your connected Things.
Need Help?
Check the Arduino Forum for questions about the Arduino Language, or how to make your own projects with Arduino. If you need any help with your board, please get in touch with the official Arduino User Support as explained on our Contact Us page.
Warranty
You can find your board warranty information here.
Tech specs
The Machine Vision Bundle includes:
Resources for Safety and Products
Manufacturer Information
The production information includes the address and related details of the product manufacturer.
Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
https://www.arduino.cc/
Responsible Person in the EU
An EU-based economic operator who ensures the product's compliance with the required regulations.
Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
Phone: +39 0113157477
Email: support@arduino.cc
Get Inspired
How to realize a servo and water sensor function using simple raw materials.
With the Earth’s population still on track to increase by another two billion people by the year 2050, finding ways to feed them without causing grave harm to the environment is a top priority. Currently, fish is one of the most widely consumed foods in the world, and a new technology called biofloc helps provide both a food source and environmental stabilizer to fishponds without the need for dangerous chemicals/processes. As part of element14’s Just Encase Design Challenge, Md. Khairul Taifur had the idea to create a device that could sit within a pond and report detailed telemetry for environmental markers, as well as correct minor water quality issues via the use of a heater and water pump. His system is based around a MKR WAN 1300, which is responsible for collecting data from each sensor and then sending it to the cloud with its onboard LoRaWAN module. To make the system independent from an external power source, Taifur included a small 5V solar panel, power management board, and rechargeable battery pack. Once his firmware had been finalized, everything was carefully placed inside of a waterproof electrical box and sealed to only expose the sensors. This biofloc monitoring system has great potential as a proof-of-concept of how future fish farms might operate in a more sustainable manner. You can read more about the project here on element14.
