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Cloud Compatible

Machine Vision Bundle

SKU h7-visionshield Barcode h7-visionshield Show more
Original price €0
Original price €154,93 - Original price €154,93
Original price
Current price €154,93
€154,93 - €154,93
Current price €154,93
VAT included

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.

TRY THE ARDUINO IOT CLOUD FOR FREE

 

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

PROJECT HUB
Tiny ML in interactive spaces Arduino X K-WAY Challenge Project
Tiny ML in interactive spaces Arduino X K-WAY Challenge Project
Project Tutorial by fullmakeralchemist

An intelligent device to track moves with responses during an interactive space with mapping, backlight, music and smart sculptures. This project makes use of a machine learning algorithm capable of tracking and detecting moves to identify associated gesture recognition through a microcontroller. Smart sculptures, lighting, music and video projection to trigger with each assigned gesture, creating a powerful AV experience highlighting the incredible potential of TinyML for the performing arts. This allows the corresponding media set Tiny ML in interactive to play when the right move was made because all these elements interact to create a new experience. This allows us to create Interactive installations, these sculptures use a combination of motors, sensors, and other electronics to create an immersive and interactive experience for the viewer. They may include projections, sound, and other sensory elements to create a complete experience.

read more
BLOG
These projects from CMU incorporate the Arduino Nano 33 BLE Sense in clever ways
These projects from CMU incorporate the Arduino Nano 33 BLE Sense in clever ways
May 22, 2023

With an array of onboard sensors, Bluetooth® Low Energy connectivity, and the ability to perform edge AI tasks thanks to its nRF52840 SoC, the Arduino Nano 33 BLE Sense is a great choice for a wide variety of embedded applications. Further demonstrating this point, a group of students from the Introduction to Embedded Deep Learning course at Carnegie Mellon University have published the culmination of their studies through 10 excellent projects that each use the Tiny Machine Learning Kit and Edge Impulse ML platform. Wrist-based human activity recognition Traditional human activity tracking has relied on the use of smartwatches and phones to recognize certain exercises based on IMU data. However, few have achieved both continuous and low-power operation, which is why Omkar Savkur, Nicholas Toldalagi, and Kevin Xie explored training an embedded model on combined accelerometer and microphone data to distinguish between handwashing, brushing one’s teeth, and idling. Their project continuously runs inferencing on incoming data and then displays the action on both a screen and via two LEDs. Categorizing trash with sound In some circumstances, such as smart cities or home recycling, knowing what types of materials are being thrown away can provide a valuable datapoint for waste management systems. Students Jacky Wang and Gordonson Yan created their project, called SBTrashCat, to recognize trash types by the sounds they make when being thrown into a bin. Currently, the model can three different kinds, along with background noise and human voices to eliminate false positives. Distributed edge machine learning The abundance of Internet of Things (IoT) devices has meant an explosion of computational power and the amount of data needing to be processed before it can become useful. Because a single low-cost edge device does not possess enough power on its own for some tasks, Jong-Ik Park, Chad Taylor, and Anudeep Bolimera have designed a system where

read more

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