Grove Vision AI v2 Kit - with Raspberry Pi OV5647 Camera Module and Seeed Studio XIAO
A high-performance, MCU-based smart vision module powered by the Himax WiseEye2 HX6538 processor, featuring a dual-core Arm Cortex-M55 with an integrated Arm Ethos-U55 neural processing unit. With Arm Helium technology optimised for vector data processing, it delivers strong ML and DSP performance at just 0.35 W — ideal for battery-powered vision AI applications.
Deploy off-the-shelf or custom AI models via the SenseCraft AI platform with no-code/low-code workflows. Supports popular frameworks including TensorFlow and PyTorch, with pre-built support for MobileNet V1/V2, EfficientNet-Lite, and YOLO v5/v8. This kit includes a Raspberry Pi OV5647 camera module and Seeed Studio XIAO for a complete development setup.
Key Features
- WiseEye2 HX6538 Processor – Dual-core Arm Cortex-M55 + Arm Ethos-U55 NPU
- 33 ms Inference Time – Fast on-device AI processing
- 30 FPS Frame Rate – Real-time vision capture and analysis
- 0.35 W Power Consumption – Suitable for battery-powered deployments
- Standard CSI Interface – Compatible with all Raspberry Pi cameras
- On-Board PDM Microphone – For sound-based AI applications
- SD Card Slot – Store images, video, and inference results
- Multiple Interfaces – I2C, UART, SPI, USB Type-C, and Grove connector
- SenseCraft AI Platform – No-code model deployment with instant visualisation
- Fully Open Source – All code, design files, and schematics available
Ideal For
- Edge AI and computer vision prototyping
- Object detection and image classification projects
- Smart home integration (e.g. Home Assistant)
- Battery-powered IoT vision applications
- Sound recognition and audio AI projects
Compatibility
- Seeed Studio XIAO series
- Arduino, Raspberry Pi, BeagleBoard, ESP-based boards
- Arduino IDE, MicroPython, CircuitPython, PlatformIO
- TensorFlow, PyTorch, MobileNet, EfficientNet, YOLO v5/v8
Package Contents
- 1× Grove Vision AI V2 Module
- 1× Raspberry Pi OV5647 Camera Module
- 1× Seeed Studio XIAO
Resources
Product Information
Product Information
Shipping & Returns
Shipping & Returns
Description
A high-performance, MCU-based smart vision module powered by the Himax WiseEye2 HX6538 processor, featuring a dual-core Arm Cortex-M55 with an integrated Arm Ethos-U55 neural processing unit. With Arm Helium technology optimised for vector data processing, it delivers strong ML and DSP performance at just 0.35 W — ideal for battery-powered vision AI applications.
Deploy off-the-shelf or custom AI models via the SenseCraft AI platform with no-code/low-code workflows. Supports popular frameworks including TensorFlow and PyTorch, with pre-built support for MobileNet V1/V2, EfficientNet-Lite, and YOLO v5/v8. This kit includes a Raspberry Pi OV5647 camera module and Seeed Studio XIAO for a complete development setup.
Key Features
- WiseEye2 HX6538 Processor – Dual-core Arm Cortex-M55 + Arm Ethos-U55 NPU
- 33 ms Inference Time – Fast on-device AI processing
- 30 FPS Frame Rate – Real-time vision capture and analysis
- 0.35 W Power Consumption – Suitable for battery-powered deployments
- Standard CSI Interface – Compatible with all Raspberry Pi cameras
- On-Board PDM Microphone – For sound-based AI applications
- SD Card Slot – Store images, video, and inference results
- Multiple Interfaces – I2C, UART, SPI, USB Type-C, and Grove connector
- SenseCraft AI Platform – No-code model deployment with instant visualisation
- Fully Open Source – All code, design files, and schematics available
Ideal For
- Edge AI and computer vision prototyping
- Object detection and image classification projects
- Smart home integration (e.g. Home Assistant)
- Battery-powered IoT vision applications
- Sound recognition and audio AI projects
Compatibility
- Seeed Studio XIAO series
- Arduino, Raspberry Pi, BeagleBoard, ESP-based boards
- Arduino IDE, MicroPython, CircuitPython, PlatformIO
- TensorFlow, PyTorch, MobileNet, EfficientNet, YOLO v5/v8
Package Contents
- 1× Grove Vision AI V2 Module
- 1× Raspberry Pi OV5647 Camera Module
- 1× Seeed Studio XIAO
