The DFRobot OpenMV Cam RT1060 is a standalone machine vision camera powered by the NXP i.MX RT1060 Cortex-M7 processor running at 600MHz. Programmed entirely in MicroPython through the OpenMV IDE, it executes face detection, color tracking, QR/barcode reading, AprilTag recognition, and TensorFlow Lite inference — all without a host computer. With 5MB RAM and a swappable M12 lens mount, it is the most capable open-source embedded vision platform in the OpenMV family. Specifications Model: OpenMV Cam RT1060 Processor: NXP i.MX RT1060 Cortex-M7 @ 600MHz RAM: 1MB SRAM + 4MB SDRAM (5MB total) Flash Storage: 16MB Camera Sensor: OV5640 5MP (default), swappable via M12 mount Resolution: Up to 5MP (2592×1944) FPS: 30fps at QVGA (320×240) for vision processing Interfaces: USB 2.0 FS, UART, SPI, I2C, CAN, GPIO Programming: MicroPython via OpenMV IDE Onboard LEDs: RGB LED + IR LEDs Dimensions: 45×36mm Key Features 600MHz Cortex-M7 — 10× faster than previous OpenMV generations Fully programmable in MicroPython via the OpenMV IDE Onboard algorithms: face detection, color tracking, AprilTag, QR/barcode 5MB RAM enables processing of higher-resolution frames Swappable lens/sensor via M12 mount Low-latency edge vision inference — no host computer required Extensive I/O: UART, SPI, I2C, CAN for robot system integration Compatible with OpenMV IDE for drag-and-drop script deployment Applications Embedded machine vision without a host PC Face detection and recognition on edge devices Color blob tracking for robotics AprilTag and QR code scanning for robot navigation Optical flow measurement for drone stabilization Edge AI inference with TensorFlow Lite models Industrial quality inspection and defect detection STEM and education machine vision curriculum Package Includes 1x DFRobot OpenMV Cam RT1060 Camera Board Note: Camera lens and mounting accessories sold separately OpenMV IDE available free at openmv.io FAQ Q: How do I program the OpenMV Cam RT1060? A: Download OpenMV IDE from openmv.io. Write MicroPython scripts, connect via USB, and click Run — no compilation needed. Q: What machine vision tasks can it perform standalone? A: Color tracking, face detection, QR/barcode reading, AprilTag detection, optical flow, and TFLite inference — all without a host computer. Q: Can I run TensorFlow Lite models? A: Yes — the RT1060 Cortex-M7 with 5MB RAM supports quantized INT8 TFLite models. Use OpenMV's tf.classify() or tf.detect() APIs. Q: Is it compatible with Arduino or Raspberry Pi? A: Yes — communicate via UART, SPI, or I2C. OpenMV can act as a vision coprocessor feeding data to Arduino or Pi. Q: Can I change the camera sensor? A: Yes — M12 lens mount allows sensor swapping (global shutter, IR, wide-angle, etc.).
