Camera AI thông minh OpenMV AE3

3D Model

• OpenMV AE3 Model (.step)

Mount

• OpenMV AE3 Mounts (.stl)

Schematic

• OpenMV AE3 Schematic (.pdf)

Datasheets

• Processor Datasheet: AE302F80F55D5AE (.pdf)
• Reference Manual: AE302F80F55D5AE (.pdf)
• Camera Brief: PAG7936 (.pdf)
• Camera Datasheet: PAG7936 (.pdf)
• Ideal Diode Datasheet: LM66100 (.pdf)
• 3.3V Regulator Datasheet: TPS62824 (.pdf)
• 2.8V Regulator Datasheet: TCR2LN28 (.pdf)
• 1.8V Regulator Datasheet: TPS62824 (.pdf)
• 1.2V Regulator Datasheet: TPS62824 (.pdf)
• Power Switch Datasheet: AP22916 (.pdf)
• FLASH Datasheet: MX25UM25645G (.pdf)
• WiFi/BLE SoC Datasheet: LBEE5KL1YN (.pdf)
• WiFi/BLE Chip Datasheet: CYW43439 (.pdf)
• IMU Datasheet: LSM6DSM (.pdf)
• MIC Datasheet: CMM-3526DB-26165 (.pdf)
• TOF Datasheet: VL53L8CX (.pdf)
• Level Shifter: NXS0102 (.pdf)
• Level Shifter: NXS0104 (.pdf)
• USB MUX Datasheet: C7USB42MU,LF (.pdf)
• USB to Serial Datasheet: FT234XD (.pdf)
• GPIO TVS Datasheet: PUSB3TB6 (.pdf)
• GPIO TVS Datasheet: DF5A6.2CJE (.pdf)
• USB EMI Filter and TVS Datasheet: EMIF02-4CMX8 (.pdf)

Certificates

• FCC Certification (.pdf)
• CE Certification (.pdf)
• RoHS Certificate: OpenMV AE3 (.pdf)
• RoHS Report: OpenMV AE3 (.pdf)

The OpenMV AE3 is a small, low power, microcontroller board which allows you to easily implement applications using machine vision in the real-world. You program the OpenMV AE3 in high level Python scripts (courtesy of the MicroPython Operating System) instead of C/C++. This makes it easier to deal with the complex outputs of machine vision algorithms and working with high level data structures. But, you still have total control over your OpenMV AE3 and its I/O pins in Python. You can easily trigger taking pictures and video on external events or execute machine vision algorithms to figure out how to control your I/O pins.

The OpenMV AE3 is our small powerhouse and features:

• The Alif Ensemble E3 Dual ARM Cortex-M55 vector accelerated processors capable of up to 8 operations per clock running at 400 MHz (3.2 gigaops) and 160 MHz (1.28 gigaops) with 13.5MB of SRAM, 5.5MB of onchip MRAM (new type of FLASH) for program execution, and 32 MB of storage FLASH. The processor features dual 400 MHz and 160 MHz NPUs offering 204 Gigaops and 46 Gigaops of AI/ML compute for running models - allowing you to run YOLO object detection algorithms onboard at 30 FPS and perform sentence recognition (for example) at the same time, and GPU for graphics acceleration that can accelerate image scaling. All I/O pins output 3.3V and are 3.3V tolerant. The processor has the following I/O interfaces:
• • A 120 FPS 1MP Color Global Shutter image sensor. Most simple algorithms will run about 60 FPS on VGA (640x480) resolutions and below. If you want to use more specialized lenses with your image sensor, you can easily buy and attach them yourself. 
  • A high speed USB-C (480Mbs) interface to your computer. Your OpenMV Cam will appear as a Virtual COM Port and a USB Flash Drive when plugged in.
  • • With EMI Filtering and TVS protection.
  • A SPI buses that can run up to 50Mb/s allowing you to easily stream image data off the system to either the LCD Shield or another microcontroller.
  • Two I2C Buses (up to 1Mb/s), an I3C Bus (up to 10 Mb/s), a CAN Bus (up to 8Mb/s), and four Asynchronous Serial Buses (TX/RX) for interfacing with other microcontrollers and sensors.
  • Two 12-bit ADCs (1.8V Tolerant).
  • Interrupts on all I/O pins (there are 10 I/O pins on the board).
  • An onboard RTC that keeps running when the system is in low-power mode.
  • • With 4KB of Backup RAM.
  • A user controllable/dimmable RGB LED.
  • A user button.
  • QWIIC Connector for easy electronic integration.
  • A B2B Header for easy electronic integration.
  • 32 MB of program/storage octal flash clocked at 100 MHz in 8-bit DDR mode for 200 MB/s of bandwidth (read speed).
  • An IMU (Accelerometer and Gyroscope) centered underneath the camera module.
  • A Mic for recording audio and sound detection/processing.
  • A 8x8 Time-of-flight (ToF) sensor capable of detecting objects up to 4 meters away.
  • Onboard WiFi (a/b/g/n - 11/54/65 Mb/s) and Bluetooth (v5.1 - BR/EDR/BLE) module with a chip antenna.
  • • Option to use a U.FL antenna instead.
• A 6-pin SWD Edge Connect Header Compatible with SEGGER J-Link Devices for debugging and programming.
• • With EMI Filtering and TVS protection.
  • Recovery switch for debugging.
• Four M1.6 mounting holes.

For more information about the OpenMV AE3, please see our documentation.

PLEASE NOTE THAT THE I/O PINS OF THE AE3 ARE NOT 5V TOLERANT! DO NOT CONNECT THE DEVICE DIRECTLY TO A 5V MCU LIKE THE ARDUINO MEGA.

You may power the AE3 using 3.3V on its 3.3V pins. The AE3 will source 3.3V from its 3.3V pins when powered by USB, unless another source already provides 3.3V.

Processor:

• Dual Core ARM® 32-bit Cortex®-M55 CPU w/ Double Precision FPU
  both w/ ARM® MVE Helium™ technology (128-bit SIMD)
• 400/160 MHz (~640/~256 DMIPS)
• Core Mark Score: 1748/752 (compare w/ Raspberry Pi 3: 3800)

Camera Info:

• PAG7936 1MP Color Global Shutter Sensor
  1280x800 @ 30 FPS
  640x400 @ 120 FPS
  320x200 @ 240 FPS
• Optical Format: 1/4"
• Pixel Size: 3um x 3um
• CRA: 20°
• SNR: 36 dB
• Dynamic Range: 64 dB

Lens Info:

• Max Image Circle Diameter: >6.8mm
• Focal Length: 3.1mm +/- 5%
• Aperture: F2.3 +/- 5%
• Back Focal Length: 1.46mm
• TV Distortion: <-%2.5
• Relative Illumination: >43.3%
• CRA: 20°
• Format: 1/2.7"
• DFOV = <87.7° =HFOV = <79°, VFOV = <49°
• Mount: M8
• IR Cut Filter: 650nm
  @430nm-595nm: Tavg > 90% Tmin > 83%
  @650nm +/- 6nm: T=50%
• Structure: 5P+IR

Actual FoV with PAG7936 image sensor:

• D72.5° H63.8° V42.3°

Electrical Info:

All pins are 3.3V tolerant with 3.3V output. All pins can sink or source up to 25mA. Do not draw more than 250mA from your OpenMV Cam's 3.3V rail.

Performance Specs:

• Power-on to inference result (attached to PC): ~2.5s
• Power-on to inference result (stand-alone): ~1.5s
• Wakeup from deepsleep to inference result (attached to PC): ~2.5s
• Wakeup from deepsleep to inference result (stand-alone): ~1.5s
• Bypass bootloader, power-on/wakeup to inference result (attached to PC): ~1.5s
• Bypass bootloader, power-on/wakeup to inference result (stand-alone): ~0.5s

Power Consumption

Full Power: 50-60mA @ 5V (0.25W-0.3W)
Idling: 24mA @ 5V (to be improved)
Deep Sleep: 80uA @ 3.3V

Dimensions

• Length: 30 mm
• Width: 30 mm
• Height: 9 mm
• Weight: 8 g

Temperature Range

• Storage Temperature: -30 °C to +85 °C
• Operating Temperature: -30 °C to +70 °C