Gravity: HUSKYLENS - AI Machine Vision Sensor
Face recognition, AI, object tracking, machine vision and much more: this sensor brings these technologies to your project
Overview
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 yout Arduino board like 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, HuskyLens 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 neural network algorithm. With these excellent performances, it is capable of capturing even fast-moving objects.
With the HuskyLens, yur projects have new ways to interact with you or 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!
HuskyLens can be 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!
HuskyLens AI Vision Sensor Useful Functions
AI Projects made simple by Husky Lens | Giveaway
DOCUMENTS
SHIPPING LIST
- HuskyLens Mainboard x1
- M3 Screws x6
- M3 Nuts x6
- Small Mounting Bracket x1
- Heightening Bracket x1
- Gravity 4-Pin Sensor Cable x1
Tech specs
- Processor: Kendryte K210
- Image Sensor: OV2640 (2.0Megapixel Camera)
- Supply Voltage: 3.3~5.0V
- Current Consumption (TYP): 320mA@3.3V, 230mA@5.0V (face recognition mode; 80% backlight brightness; fill light off)
- Connection Interface: UART, I2C
- Display: 2.0-inch IPS screen with 320*240 resolution
- Built-in Algorithms: Face Recognition, Object Tracking, Object Recognition, Line Tracking, Color Recognition, Tag Recognition
- Dimension: 52mm * 44.5mm / 2.05 x 1.75inch
Get Inspired
Arduino based bicycle movement sensor and GPS tracker.
… magnetic reed switch, but building the game himself in Unity. He had to construct and animate models for himself, the bike, and the scenery. After adding an AI and ranking system to the game, he was able to successfully race within the virtual environment on a real bike. Responsive LED system Motivated by the desire for a more advanced lighting system while on her nighttime bike rides, Natasha (TechnoChic) decided to affix strips of NeoPixel LEDs all over her bike that could react to music in real-time. The LEDs are controlled by an Arduino Nano 33 IoT that is, in turn, connected to her boombox via a 3.5mm audio jack for reading the audio signal. Two additional Nano 33 IoT boards were used for the wheels, along with more NeoPixels and batteries for each. GPS tracker Bicycle theft has been rapidly increasing over the last couple of years, which is why being able to recover a stolen bike has become vital. Johan’s bike tracker project contains an Arduino MKR GSM 1400 which reads motion data from an IMU and uses it to determine if the bike has moved when it is not supposed to. Once movement is detected, the board reads GPS data from a MKR GPS Shield and sends it over an LTE data connection in real-time so that the bike can be found. Integrated safety features The majority of mountain bikes lack useful safety features such as integrated lights, turn signals, and speed tracking, which is why Collin Wentzien embarked on his “(not so) electric bike” project. He built a series of features, including automatic brake/turn lights, a headlight, and an electronic horn with the goal of improving safety. Furthermore, his bike also got a bike computer upgrade which contains an Arduino Mega, GPS module, and dual screens for displaying relevant telemetry data. Speedometer display After losing the display unit for her bike computer, Element14 Presents host Katie wanted to replace it with a DIY version that tracked the current speed
