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Portenta Vision Shield - Ethernet

SKU ASX00021 Barcode 7630049202481 Show more
Original price €0
Original price €56,25 - Original price €56,25
Original price
Current price €56,25
€56,25 - €56,25
Current price €56,25
VAT included

Professional computer vision, directional audio detection, Ethernet, and JTAG for Arduino Portenta.
The Portenta Vision Shield is also available with LoRa® connectivity. Check it out here!

Overview

The Portenta Vision Shield brings industry-rated features to your Portenta. This hardware add-on will let you run embedded computer vision applications, connect wirelessly or via Ethernet to the Arduino Cloud or your own infrastructure, and activate your system upon the detection of sound events.

The shield comes with:

  • a 320x320 pixels camera sensor: use one of the cores in Portenta to run image recognition algorithms using the OpenMV for Arduino editor
  • a 100 Mbps Ethernet connector: get your Portenta H7 connected to the wired Internet
  • two on-board microphones for directional sound detection: capture and analyze sound in real time
  • JTAG connector: perform low-level debugging of your Portenta board or special firmware updates using an external programmer
  • SD-Card connector: store your captured data in the card, or read configuration files

The Vision Shield Ethernet has been designed to work with the Portenta H7. The Portenta boards feature multicore 32-bit ARM® Cortex® processors running at hundreds of megahertz, with megabytes of program memory and RAM. Portenta boards come with WiFi and Bluetooth®. Purchase this Shield together with the Portenta H7 for full performance.

Embedded Computer Vision Made Easy

Arduino has teamed up with OpenMV to offer you a free license to the OpenMV IDE, an e

asy way into computer vision using MicroPython as a programming paradigm. Download the OpenMV for Arduino Editor from our professional tutorials site and browse through the examples we have prepared for you inside the OpenMV IDE. Companies across the whole world are already building their commercial products based on this simple-yet-powerful approach to detect, filter, and classify images, QR codes, and others.


QR code detection example

Blob analysis example

Debugging With Professional Tools

Connect your Portenta H7 to a professional debugger through the JTAG connector. Use professional software tools like the ones from Lauterbach or Segger on top of your board to debug your code step by step. The Vision Shield exposes the required pins for you to plug in your external JTAG.

Getting Started

The Portenta tutorials section at the Arduino Docs website contains all the information you need to configure the Portenta H7, as well as the Vision Shield, and the OpenMV editor for computer vision applications.

Need Help?

Check the Arduino Forum for questions about the Arduino Language, or how to make your own Projects with Arduino. Need any help with your board please get in touch with the official Arduino User Support as explained in our Contact Us page.

Warranty

You can find here your board warranty information.


Tech specs

The Arduino Vision Shield is an active add-on to the Portenta family of boards.

Camera Himax HM-01B0 camera module (manufacturer site)
Resolution 320 x 320 active pixel resolution with support for QVGA
Image sensor High sensitivity 3.6μ BrightSense™ pixel technology
Microphone 2 x MP34DT05 (datasheet)
Length 66 mm
Width 25 mm
Weight 11 gr

Conformities

The following Declarations of Conformities have been granted for this board:
RCM
RoHS
CE
FCC
UKCA
REACH
WEEE
For any further information about our certifications please visit docs.arduino.cc/certifications

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

 

Documentation

 

Learn more about the Portenta Vision Shield using the following files:

SCHEMATICS IN .PDF DATASHEET IN .PDF

 

 

Pinout Diagram

Download the full pinout diagram as PDF here.

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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