UnitV2-M12

SKU:U078-M12

Description

UnitV2-M12 is a high-efficiency AI recognition module launched by M5Stack, featuring the Sigmstar SSD202D (integrated dual-core Cortex-A7 1.2GHz processor) as the control core, integrated 128MB-DDR3 memory, 512MB NAND Flash, and a 1080P camera. It is equipped with 1x standard focal length (FOV:85°) + 1x wide-angle fisheye lens (FOV:150°) two M12 universal specification lenses, supporting manual focus. It comes with an embedded Linux operating system, integrated with rich software and hardware resources and development tools, aiming to provide users with an out-of-the-box, simple and efficient AI development experience.

Features

• Sigmstar SSD202D
• Dual-core Cortex-A7 1.2GHz processor
• 128MB DDR3
• 512MB NAND Flash
• GC2053 1080P Colored Sensor
• Equipped with dual lenses: standard focal length (FOV:85°) + wide-angle fisheye lens (FOV:150°)
• Built-in microphone
• Wi-Fi 2.4GHz
• Development methods:
  • Comes with 12 common AI image functions: QR code, face detection, line tracking, motion detection, shape matching, image streaming, classification, color tracking, face recognition, object tracking, shape detection, custom object recognition
  • Supports web online preview, UIFlow (used as serial port JSON format call)
  • Linux environment (OpenCV, SSH, JupyterNotebook)

Includes

• 1 x UnitV2-M12
• 1 x 16GB MicroSD card
• 1 x USB Type-C cable (50cm)
• 1 x Stand
• 1 x Clip
• 1 x Standard focal length lens (FOV:85°)
• 1 x Wide-angle fisheye lens (FOV:150°)

Applications

• AI recognition function development
• Industrial visual recognition and classification
• Machine vision learning

Specifications

Schematics

Softwares

Quick Start

• UnitV2 integrates the basic AI recognition service developed by M5Stack, with built-in multiple recognition functions (such as face recognition, object tracking, etc.), which can quickly help users build AI recognition applications.

• All functions! Plug and play! UnitV2 has a built-in wired network card. When you connect it to a PC via the TypeC interface, it will automatically establish a network connection with UnitV2. It can also be connected and debugged via Wi-Fi, offering high flexibility.

• UART serial output, all recognition content is automatically output in JSON format via the serial port, making it easy to call.

• Built-in recognition function usage tutorial

• V-Training online AI model training service

• Jupyter Notebook development tutorial/Example

• SSH connection & WIFI configuration

• Firmware update tutorial

SDK

• UnitV2Framework

USB Driver

For Windows10

• Extract the driver package to the desktop path -> Go to the device manager and select the currently unrecognized device (named USB 10/100 LAN or with SR9900 characters) -> Right-click and select custom update -> Select the path where the package was extracted -> Click confirm and wait for the update to complete.

For MacOS

• Extract the driver package -> Double-click to open the SR9900_v1.x.pkg file -> Follow the prompts to click next to install. (The package contains a detailed version of the driver installation tutorial pdf)

• After installation, if the network card cannot be enabled normally, you can open the terminal and use the following command to re-enable the network card.

sudo ifconfig en10 down
sudo ifconfig en10 up

Other

Object Detection using M5Stack UnitV2 and Edge Impulse

Object Detection using M5Stack UnitV2 and Edge Impulse

Using M5Stack's latest Linux AI smart camera UnitV2, build a simulated industrial application scenario for defective product screening.

Video

UnitV2 Built-in Functions Unboxing and Usage

UnitV2 Application Scenarios

Product Comparison