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Cloud Compatible

Nicla Vision

SKU ABX00051 Barcode 7630049203037 Show more
Original price €0
Original price €116,85 - Original price €116,85
Original price
Current price €116,85
€116,85 - €116,85
Current price €116,85
VAT included

Deploy computer vision at the edge faster than ever, with our ready-to-use, standalone intelligent camera.

Overview

Nicla Vision allows you to build your next smart project. Ever wanted an automated house? Or a smart garden? Well, now it’s easy with the Arduino IoT Cloud compatible boards. It means: you can connect devices, visualize data, control and share your projects from anywhere in the world. Whether you’re a beginner or a pro, we have a wide range of plans to make sure you get the features you need.
 

Nicla Vision combines a powerful STM32H747AII6 Dual ARM® Cortex® M7/M4 IC processor with a 2MP color camera that supports TinyML, as well as a smart 6-axis motion sensor, integrated microphone and distance sensor.
You can easily include it into any project because it’s designed to be compatible with all Arduino Portenta and MKR products, fully integrates with OpenMV, supports MicroPython and also offers both WiFi and Bluetooth® Low Energy connectivity. It’s so compact – with its 22.86 x 22.86 mm form factor – it can physically fit into most scenarios, and requires so little energy it can be powered by battery for standalone applications. 

All of this makes Nicla Vision the ideal solution to develop or prototype with on-device image processing and machine vision at the edge, for asset tracking, object recognition, predictive maintenance and more – easier and faster than ever. Train it to spot details, so you can focus on the big picture.



Key benefits include:
  • Tiny form factor of 22.86 x 22.86 mm
  • Powerful processor to host intelligence on the edge
  • Packed with a 2MP color camera that supports TinyML, smart 6-axis motion sensor, microphone and distance sensor
  • Wi-Fi and Bluetooth® Low Energy connectivity
  • Supports MicroPython
  • Standalone when battery powered
  • Expand existing project with sensing capabilities, make MV prototyping faster
Automate anything

Check every product is labeled before it leaves the production line; unlock doors only for authorized personnel, and only if they are wearing PPE correctly; use AI to train Nicla Vision to regularly check analog meters and beam readings to the Cloud; teach it to recognize thirsty crops and turn the irrigation on when needed.
Anytime you need to act or make a decision depending on what you see, let Nicla Vision watch, decide and act for you.

Feel seen

Interact with kiosks with simple gestures, create immersive experiences, work with cobots at your side. Nicla Vision allows computers and smart devices to see you, recognize you, understand your movements and make your life easier, safer, more efficient, better.

Keep an eye out

Let Nicla Vision be your eyes: detecting animals on the other side of the farm, letting you answer your doorbell from the beach, constantly checking on the vibrations or wear of your industrial machinery.
It’s your always-on, always precise lookout, anywhere you need it to be. 

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 in our Contact Us page.

Warranty

You can find your board warranty information here.


Tech specs

Microcontroller STM32H747AII6 Dual Arm® Cortex® M7/M4 IC:
  • 1x  Arm® Cortex® M7 core up to 480 MHz
  • 1x Arm® Cortex® M4 core up to 240 MHz
Sensors
  • 2 MP Color Camera
  • 6-Axis IMU (LSM6DSOX)
  • Distance / Time Of Flight sensor (VL53L1CBV0FY/1)
  • Microphone (MP34DT05)
I/O Castellated pins with the following features:
  • 1x I2C bus (with ESLOV connector), JTAG, Power and GPIO pin headers
  • 1x serial port
  • 1x SPI
  • 2x ADC
  • Programmable I/O voltage from 1.8-3.3V
Power
  • High speed USB (480Mbps)
  • Pin Header
  • 3.7V Li-po battery with Integrated battery charger and fuel gauge (MAX17262REWL) 
Dimensions 22.86 mm x 22.86  mm
Memory 2MB Flash / 1MB RAM 
16MB QSPI Flash for storage 
Security NXP SE050C2 Crypto chip
Connectivity Wi-Fi / Bluetooth® Low Energy 4.2
(Murata 1DX - LBEE5KL1DX-883)
Interface USB interface with debug functionality
Operating temperature -20° C to +70° C (-4° F to 158°F)

 

Conformities

The following Declarations of Conformities have been granted for this board:
REACH
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

SCHEMATICS IN .PDFPINOUT IN .PDFDATASHEET 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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