TinyML Image Recognition with OV7670 Camera Module
As part of my Google Summer of Code 2022 project, I created a comprehensive tutorial on building TinyML image recognition systems using the OV7670 camera module. This project demonstrates how to implement computer vision on resource-constrained devices.
Project Overview
This tutorial guides you through building a complete TinyML image recognition system that can run on microcontrollers with minimal power consumption. Perfect for IoT applications where cloud connectivity isn’t available or practical.
What You’ll Build
- Real-time Image Capture: Using OV7670 camera module
- On-device ML Inference: TensorFlow Lite models running on Arduino
- Low Power Operation: Optimized for battery-powered applications
- Portable System: Compact design suitable for embedded applications
Key Components
- OV7670 Camera Module: Low-cost VGA camera sensor
- Arduino Nano 33 BLE Sense: Microcontroller with built-in ML capabilities
- TensorFlow Lite Micro: Optimized ML framework for microcontrollers
- Edge Impulse: Platform for training and deploying ML models
Technical Highlights
- Image preprocessing and optimization techniques
- Model quantization for efficient inference
- Memory management strategies for constrained devices
- Real-time performance optimization
This project has been incredibly popular in the maker community, helping thousands of developers get started with TinyML and embedded computer vision.