Huskylens AI-Camera Sensor Kit

Huskylens AI-Camera Sensor Kit

$82.00

Out of stock

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  • One-Click-Learn: HuskyLens is designed to be smart. Built-in algorithms allow HuskyLens to learn new things just by a single click.
  • Machine-Learning-Enabled: Equipped with advanced machine learning technology, HuskyLens is capable of recognizing faces and objects, which is far more beyond ordinary sensors.
  • Onboard Screen: HuskyLens carries a 2.0 inch IPS screen, therefore you don’t need to use a PC in parameters tuning. Enjoy the convenience it brings, what you see is what you get!
  • Extreme Performance: HuskyLens adopts a new generation AI specialized chip Kendryte K210, contributing to 1,000 times faster performance compared to STM32H743 when running neural network algorithm.
  • Comaptible with Arduino, Raspberry, Microbit

  • HuskyLens is an easy-to-use AI machine vision sensor. It is equipped with multiple functions, such as face recognition, object tracking, object recognition, line tracking, color recognition, and tag(QR code) recognition.
  • Through the UART / I2C port, HuskyLens can connect popular main control boards like Arduino, micro: bit, Raspberry Pi and LattePanda to help you make very creative projects without playing with complex algorithms.
  • HuskyLens is pretty easy to use. You can change various algorithms by pressing the function button. Click the learning button, Husky lens starts learning new things. After that, HuskyLens is able to recognize them.
  • Additionally, HuskyLens carries a 2.0 inch IPS screen. So you don’t need to use a PC in the parameters tuning. Enjoy the convenience it brings, what you see is what you get!

  • HuskyLens is designed to be smart. It has the built-in machine learning technology that enables HuskyLens to recognize faces and objects. Moreover, by long-pressing the learning button, HuskyLens can continually learn new things even from different angles and in various ranges. The more it learns, the more accurate it is.
  • HuskyLens adopts the new generation of specialized AI chip Kendryte K210. The performance of this special AI chip is 1,000 times faster than that of the STM32H743 when running a neural network algorithm. With these excellent performances, it is capable of capturing even fast-moving objects.
  • With the HuskyLens, your projects have new ways to interact with you or the environment, such as interactive gesture control, autonomous robot, smart access control, and interactive toy. There are so many new applications for you to explore.
  • HuskyLens’ object-tracking skills can be used to learn specific gestures. It is able to recognize those learned hand movement patterns and feed their positions. With these data, creating awesome interactive projects are never so easy.

  • HuskyLens can detect and follow lines. Line follower is not something new, there are plenty of excellent methods and algorithms in this scenario. However, most of them require tedious parameters tuning. This time, HuskyLens provides a new way to do line following: simply click the button, then it starts learning and tracking new lines. Let’s enjoy the fun of making with HuskyLens!
  • Husky lens machines can be the eyes of robots. which allows your robot to recognize you, understand your hand gesture commands, or help you put stuff in order, and so on. With Huskylens, nothing is impossible
  • Husky lens machines can be the eyes of robots. which allows your robot to recognize you, understand your hand gesture commands, or help you put stuff in order, and so on. With Huskylens, nothing is impossible!

Included

  • HuskyLens Mainboard x1
  • M3 Screws x6
  • M3 Nuts x6
  • Small Mounting Bracket x1
  • Heightening Bracket x1
  • Gravity 4-Pin Sensor Cable x1

References:

  1. Huskylens Object tracking – Arduino Servo Pan and Tilt – YouTube
  2. DIY Robot with AI vision sensor for Object classification – YouTube
  3. Insane Arcade Project using Attract Mode and Dynaframes – YouTube
  4. Easy Machine Learning on Arduino/Raspberry Pi with DFRobots HuskyLens! – YouTube
  5. Line following tutorial

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