HuskyLens K210 AI Vision Sensor

1. Board Overview

The article 'Board Overview' offers a comprehensive guide on HuskyLens connectors, detailing the functionalities of USB and 4-pin connectors, and explains the operations of the function and learning buttons for optimal device usage.

2. Upgrade Firmware

This article guides users through upgrading HuskyLens firmware to enhance functionality and experience, covering procedures for Windows, Linux, and Mac OS, including checking firmware versions, downloading necessary drivers, and using specific tools for a successful upgrade.

3. General Settings

The article explains how to operate Huskylens general settings, including selecting, adjusting, and saving various parameters like protocol type, screen brightness, LED, and RGB light settings. It also covers menu auto-hide duration, factory reset, and language selection, ensuring users can effectively customize their device for optimal performance.

4. Color and Coordinate

This article delves into HuskyLens' use of color definitions to indicate the status of object and face recognition, alongside the implementation of a coordinate system to locate the position of detected objects. Readers will gain insights into how the RGB LED indicator and coordinate data from UART/I2C ports facilitate accurate learning and recognition processes.

5. Functions Introduction-Face Recognition

This article provides a comprehensive guide on using HuskyLens for face recognition, detailing how to detect, learn, and recognize faces, both individually and in groups, with practical tips for adjusting angles and settings to enhance recognition accuracy.

6. Functions Introduction-Object Tracking

This article introduces the object tracking function of Huskylens, detailing the operation and setting procedures, and providing a step-by-step guide on learning and detecting objects effectively.

7. Function Introduction-Object Recognition

This article introduces the object recognition function of HuskyLens, detailing how it can identify and track 20 built-in objects, with a focus on marking and recognizing multiple objects effectively.

8. Function Introduction-Line Tracking

This article details the line tracking function, focusing on tracking single color lines with path prediction, and includes settings, learning, and detection tips to optimize performance.

9. Function Introduction-Color Recognition

Explore the comprehensive guide on color recognition using HuskyLens, covering techniques to learn, recognize, and track multiple colors, adjust settings for accuracy, and understand firmware capabilities for enhanced functionality.

10. Function Introduction-Tag Recognition

The article explains how to use the Tag Recognition function to detect, learn, and track April Tags, with a focus on setting parameters for multiple tag recognition and providing a step-by-step guide for operation.

11. Function Introduction-Object Classification

The article explains HuskyLens' object classification function, which uses machine learning algorithms to recognize objects by learning multiple photos from different angles and distances. It demonstrates how to set up and operate the classification function, using helmet detection as an example, to enhance recognition accuracy.

12. Auxiliary Function-Customize ID Name

The article provides a detailed guide on customizing ID names for enhanced recognition using algorithms, supporting only English names. It includes demonstration programs for Mind+, MakeCode, and Arduino, showcasing how to change 'Face:ID1' to 'Jack:ID1', improving the recognizability of objects and people.

13. Auxiliary Function-Displaying Customized Text on Screen

This article explains how to utilize onboard screens to display customized text, providing sample programs for Arduino, MakeCode, and Mind+ to enhance user interaction by displaying sensor data and recognition results directly on the screen.

14. Auxiliary Function-Saving Photos or Screenshots into SD Card

This article presents a guide on using HUSKYLENS to save photos and screenshots to an SD card, detailing necessary steps and providing sample code for micro:bit and Arduino.

15. Auxiliary Function-SD Card saving/loading models

This article explores how HUSKYLENS uses SD cards to save and load models, allowing efficient data management across multiple algorithms. It covers manual and automated methods to export/import models, with detailed steps on using micro:bit, Arduino, Mind+, and MakeCode for model management.

16. Auxiliary Function-Use Program to Trigger Learning Function

This article details how to programmatically trigger the learning functions of HUSKYLENS using microcontrollers like Arduino and micro:bit, showcasing sample programs and automation techniques for efficient object recognition.

17. Example Code for Arduino-Read Position Data(UART)

This article demonstrates how to connect a HuskyLens AI vision sensor to an Arduino Uno using UART to read position data of objects in real time, complete with hardware preparation, software setup, and sample code, making it perfect for object tracking projects.

18. Example Code for Arduino-Read Position Data(I2C)

This article guides you through the process of connecting a HuskyLens AI machine vision sensor to an Arduino board using the I2C protocol. It provides detailed instructions on hardware setup, software installation, and example code to help you read real-time position data of objects via the Arduino serial monitor.

19. Example Code for micro:bit-Face Recognition(Mind+)

This article details the process of connecting HuskyLens to micro:bit for face recognition using Mind+, covering hardware setup, software preparation, and coding to display results on the micro:bit's dot-matrix screen.

20. Example Code for micro:bit-Face Recognition(MakeCode)

This article explains how to connect HuskyLens to the micro: bit board for face recognition using MakeCode, detailing the hardware and software setup, wiring instructions, and sample code to ensure a successful implementation where the micro: bit displays a smiling or crying face based on recognition results.